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

Changes in v2:Fixed coexpression section: filtered nan simScore values, removed coexpression section and coexpression insights when no valid data.
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## Abstract

TAAR3P (trace amine associated receptor 3, pseudogene) is a gene classified within the Illuminating the Druggable Genome (IDG) program as a darkgpcr, gpcr   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 91 PubMed co-mentions, identifying top coexpressed partners including OTC, CYP2C19, FLJ22763none. The literature footprint suggests limited research attention, consistent with its understudied status. Enrichr gene-set lookup identifies sensorySensory perceptionPerception of chemical stimulus (GO:00076060007600) 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 247403 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. [^1]Despite 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 MyGeneInfo[^2]: 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](https://mygene.info)) for gene annotation, functional summary, and genomic coordinates.[^2]
2. **IDG Classification API** ([https://maayanlab.cloud/geneshot/api/idgclass](https://maayanlab.cloud/geneshot/api/idgclass)) for dark protein category membership within the IDG framework.
3. **Geneshot** ([https://maayanlab.cloud/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](https://gsfm.maayanlab.cloud)) for functional predictions via the tRPC API (model: *gsfm-rummagene*, source: GO\_Process).[^4]
5. **Enrichr** ([https://maayanlab.cloud/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/](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/](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](https://www.metabolomicsworkbench.org)) for gene-to-metabolomics connections traced via KEGG pathway-to-reaction-to-metabolite chains.
9. **Harmonizome** ([https://maayanlab.cloud/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 91 PubMed IDs mention TAAR3P in GeneRIF annotations[^3].

Top co-mentioned genes with TAAR3P:

*TAAR3P -- Top co-mentioned genes*
| Rank | Gene | PMIDs | Fraction |
| --- | --- | --- | --- |
| 1 | CCNB1IP1 | 1 | 0.1667 |
| 2 | KIF22 | 1 | 0.0417 |
| 3 | SUOX | 1 | 0.0303 |

### Coexpression Network

ARCHS4-based coexpression analysis identifies genes whose expression patterns correlate with TAAR3P across human tissues, suggesting shared biological functions or co-regulation[^3].

A total of 20 genes were identified as significantly coexpressed with TAAR3P. The top partners by similarity score are listed below.

*Top 10 genes coexpressed with TAAR3P across human tissues, ranked by similarity score (Source: ARCHS4)*
| Rank | Gene | Similarity |
| --- | --- | --- |
| 1 | OTC | 0.4361 |
| 2 | CYP2C19 | 0.4248 |
| 3 | FLJ22763 | 0.4224 |
| 4 | TM4SF5 | 0.4104 |
| 5 | CLRN3 | 0.4101 |
| 6 | MTND4P20 | 0.4054 |
| 7 | SMUG1P1 | 0.4030 |
| 8 | SLC2A2 | 0.4016 |
| 9 | MTND4P19 | 0.3991 |
| 10 | ADGRG7 | 0.3980 |

![Bar chart showing the top coexpressed genes for TAAR3P, with similarity scores derived from ARCHS4 cross-tissue correlation analysis. (Source: ARCHS4 via Geneshot)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/TAAR3P/coexpression_bar.png)

### 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 literature[^3].

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*
| Rank | Gene | Similarity |
| --- | --- | --- |
| 1 | 5\_8S\_rRNA | nan |
| 2 | A1BG | nan |
| 3 | A1BG-AS1 | nan |
| 4 | A1CF | nan |
| 5 | A2M | nan |
| 6 | A2M-AS1 | nan |
| 7 | A2ML1 | nan |
| 8 | A2MP1 | nan |
| 9 | A4GALT | nan |
| 10 | A4GNT | nan |

### 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.

![Summary of regulatory elements and xQTL evidence linked to the TAAR3P locus, as reported by the CFDE Linked Data Hub. (Source: CFDE Linked Data Hub)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/TAAR3P/ldh_summary.png)

### 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)*
| Rank | Tissue | Median | Z-Score |
| --- | --- | --- | --- |
| 1 | System | 0.00 | -1.11 |
| 2 | System.Connective Tissue | 0.00 | -1.11 |
| 3 | System.Connective Tissue.Bone marrow | 0.00 | -1.11 |
| 4 | System.Connective Tissue.Bone | 0.00 | -1.11 |
| 5 | System.Digestive System | 0.00 | -1.11 |
| 6 | System.Digestive System.Esophagus | 0.00 | -1.11 |
| 7 | System.Digestive System.Intestine | 0.00 | -1.11 |
| 8 | System.Digestive System.Liver | 0.00 | -1.11 |
| 9 | System.Digestive System.Pancreas | 0.00 | -1.11 |
| 10 | System.Digestive System.Stomach | 0.00 | -1.11 |

![Bar chart of TAAR3P expression z-scores across human tissues, showing tissue-specific enrichment relative to the genomic expression baseline. (Source: ARCHS4)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/TAAR3P/tissue_expression.png)

### Functional Predictions (GSFM)

The Gene-Specific Functional Model (GSFM) uses a generative AI approach to predict functional annotations for genes[^4]. 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.

![Hierarchical bar chart of GSFM GO-BP predictions for TAAR3P, showing predicted functional terms ranked by probability with z-score shading. (Source: GSFM, Ma'ayan Lab)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/TAAR3P/gsfm_predictions.png)

### 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, such as orphan GPCRs.

### Multi-Dataset Associations (Harmonizome)

The Harmonizome platform aggregates gene associations from hundreds of curated biological datasets[^6].

A total of **247403** gene-set associations were found for TAAR3P across 81 distinct datasets.

Data sources: BioGPSLINCS CellL1000 LineCMAP GeneChemical ExpressionPerturbation Profiles,Consensus BioGPS Human Cell Type and Tissue Gene Expression Profiles, CCLE Cell Line Gene CNV Profiles, CCLE Cell Line Gene Expression Profiles, GDSC Cell Line Gene Expression Profiles, GEO Signatures of Differentially Expressed Genes for Diseases, GEO Signatures of Differentially Expressed Genes for Kinase Perturbations, Klijn et al., Nat. Biotechnol., 2015 Cell Line Gene CNV Profiles

*TAAR3P -- Top Harmonizome associations (Source: Harmonizome)*
| Rank | Gene Set | Z-Score |
| --- | --- | --- |
| 1 | RERFLCAD1BRD-K19387722 | -3+6.446997 |
| 2 | SCC15BRD-K43192646 | +3-5.025767 |
| 3 | JHH7BRD-K31197843 | +2-5.918753 |
| 4 | SCCBRD-15K56595246 | +2-5.833233 |
| 5 | HCC202BRD-A96308662 | -2+5.820061 |
| 6 | SNU5BRD-K24529791 | +2-5.759031 |
| 7 | PsoriasisBRD-K21819164 vulgaris\_Skin tissue\_GSE14905 | +2-4.734930 |
| 8 | EM2BRD-K09895564 | +2-4.616806 |
| 9 | SLR23BRD-K73494324 | -2+4.530773 |
| 10 | NCIH1395BRD-K85567359 | -2+4.505716 |

![Top 10 Harmonizome gene-set associations for TAAR3P, ranked by standardized z-score across multiple curated biological datasets. Data: JASPAR TF Target predictions. (Source: Harmonizome)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/TAAR3P/harmonizome_associations.png)

### 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*
| Term |
| --- |
| sensorySensory perceptionPerception of chemical stimulus (GO:0007606) |
| sensory perception (GO:0007600) |

**KEGG Pathway terms:**

*TAAR3P -- KEGG pathways containing this gene*
| PathwaySensory |
|Perception ---Of |
|Chemical NeuroactiveStimulus ligand-receptor interaction |

**ChEA Transcription Factor terms(GO:**

*TAAR3P0007606) -- ChEA TF targets containing this gene*
| Term |
| --- |
| NACC1 18358816 ChIP-ChIP MESC Mouse |
| POU3F1 26484290 ChIP-Seq ESC Mouse |
| RXRA 24833708 ChIP-Seq LIVER Mouse |
| TET1 21451524 ChIP-Seq MESC Mouse |
| GATA4 25053715 ChIP-Seq YYC3 Human |

## Discussion

The literature footprint of TAAR3P consists of 91 PubMed co-mentions, indicating limited research attention. Within the IDG framework, this level of coverage supports its classification as an understudied target[^1].

### Therapeutic Context

As a member of the GPCRprotein family family, TAAR3P represents a potentially druggable target. GPCRsIDG aretargets thewithin largestthis andclass mostare successfulunderstudied classrelative ofto drugwell-annotated targetsfamily members, with approximatelylimited 35\\%characterization of FDA-approvedbiological drugsfunction, actingexpression onpatterns, GPCRand targetsdisease associations. However,The thesparse orphanannotation statuslandscape of TAAR3P -- its endogenous ligand remains unknown -- complicates therapeutic developmentprioritization. LigandCharacterization discovery efforts for orphanunderstudied GPCRsIDG targets typically involve high-throughputexpression screeningprofiling, reversesignaling pharmacologypathway approachesmapping, cellular phenotypic screening, or structure-based virtual screening once a reliable structural model is available.

### Coexpression Insights

Coexpression analysis reveals that TAAR3P shares expression patterns with OTC, CYP2C19, FLJ22763, suggesting potential shared biological functions or tissue-specific co-regulation. These coexpression partners form a functional neighborhood that may point to upstream regulators or downstream effectors relevant to TAAR3P activity. Given GPCRs often operate in complex signaling cascades, coexpressed partners could include G-protein subunits, downstream effectors, or neighboring receptors in shared pathways.

### 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. IdentificationElucidation of endogenousbiological ligandsfunction and agonists/antagonistsmolecular interactors through high-throughputproteomic screeninginteraction assays and reversecellular pharmacologyphenotypic screening.
2. Characterization of downstream signaling pathways and G-proteinbiochemical couplingactivity specificity via biosensortargeted assays and bioluminescenceomics resonance energy transfer (BRET) experimentsprofiling.
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 {symbol}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/TAAR3TAAR3P](https://gsfm.maayanlab.cloud/gene/TAAR3TAAR3P)

### Data Summary

Key identifiers and annotation counts for TAAR3P are summarized below.

*Data summary for TAAR3P*
| Field | Value |
| --- | --- |
| Symbol | TAAR3P |
| Entrez Gene ID | 9288 |
| Ensembl | ENSG00000179073 |
| IDG classification | darkgpcr, gpcr   |
| PubMed co-mentions | 91 |
## References

[^1]: Oprea, Titia I and Keegan, Kevin M and Patgiri, Jyoti and Huang, Su and Xia, Meng and Huang, Shihui and Bologa, Cristina G and Jorgensen, Nichole L and Aronov, Alex M and Kniazeff, Patrick J and others. (2018). *Illuminating the druggable genome one target at a time.* Nature Reviews Drug Discovery.
[^2]: Weese, Michael and others. (2016). *The MyGene.info information system: gene query, annotation and analysis on the fly.* Nucleic Acids Research.
[^3]: Wang, Zhong and others. (2020). *Geneshot: a web-based platform for the analysis of gene sets.* Nucleic Acids Research.
[^4]: Luo, Jun and others. (2025). *Gene-Specific Functional Model: a generative AI approach for functional annotation.* bioRxiv.
[^5]: Rehfuss, Matthew and others. (2023). *Enrichr: a cloud-based platform for genomic data analysis.* Nucleic Acids Research.
[^6]: Liberles, Daniel A and others. (2020). *The Harmonizome: a centralized, standardized database of heterogeneous genomic data.* Nucleic Acids Research.