A Multi-Source Profile of GPHRA: golgi pH regulator A as an IDG Understudied Ion Channel

Abstract

GPHRA (golgi pH regulator A) is a gene classified within the Illuminating the Druggable Genome (IDG) program as a darkionchannel, ionchannel 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 4 PubMed co-mentions, identifying top coexpressed partners including GPR89B, ALG8, NOL10. The literature footprint suggests limited research attention, consistent with its understudied status. Enrichr gene-set lookup identifies intracellular pH reduction (GO:0051452) as a leading annotation. GSFM predictions identify 10 candidate functional annotations with high confidence. No regulatory element data were available for this locus. Multi-dataset association analysis via the Harmonizome identifies 2345 gene-set associations across multiple biological datasets.Together, these findings provide a comprehensive baseline for future investigation of GPHRA 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

GPHRA is encoded by a gene with Entrez ID 653519 (Ensembl: ENSG00000117262). According to MyGeneInfo²: GPR89A is a nearly identical copy of the GPR89B gene (MIM 612806).[supplied by OMIM, Jun 2009]

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 4 PubMed IDs mention GPHRA in GeneRIF annotations³.

Coexpression Network

ARCHS4-based coexpression analysis identifies genes whose expression patterns correlate with GPHRA across human tissues, suggesting shared biological functions or co-regulation³.

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

Top 10 genes coexpressed with GPHRA across human tissues, ranked by similarity score (Source: ARCHS4)

Rank	Gene	Similarity
1	GPR89B	0.5539
2	ALG8	0.5185
3	NOL10	0.5007
4	DCAF13	0.4976
5	ORC5	0.4958
6	NUP35	0.4930
7	NOP58	0.4925
8	BRIX1	0.4909
9	NDUFAF6	0.4860
10	UMPS	0.4847

Literature-Based Gene Similarity

GeneRIF-based similarity identifies genes that share biological context with GPHRA 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 GPHRA, ranked by GeneRIF similarity score

Rank	Gene	Similarity
1	SNORA11B	0.0455
2	SND1	0.0380
3	LINC02337	0.0250
4	RNU6-1118P	0.0164
5	EHF	0.0116
6	LINC02115	0.0097
7	OR8K3	0.0071
8	GPATCH2L	0.0052
9	MORN1	0.0044
10	MTDH	0.0034

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 GPHRA. Regulatory elements include enhancers, promoters, and insulators whose activity profiles overlap or target the GPHRA locus.

No regulatory elements or xQTL evidence were identified for the GPHRA 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 GPHRA, derived from ARCHS4 cross-tissue analysis (Source: ARCHS4)

Rank	Tissue	Median	Z-Score
1	PLASMA CELL	9.42	1.30
2	INTESTINAL EPITHELIAL CELL	8.80	1.14
3	System	0.00	-1.11
4	System.Connective Tissue	0.00	-1.11
5	System.Connective Tissue.Bone marrow	0.00	-1.11
6	System.Connective Tissue.Bone	0.00	-1.11
7	System.Digestive System	0.00	-1.11
8	System.Digestive System.Esophagus	0.00	-1.11
9	System.Digestive System.Intestine	0.00	-1.11
10	System.Digestive System.Liver	0.00	-1.11

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 GPHRA, returning predicted terms with associated probabilities, z-scores, and known-annotation status.

A total of 10 GO-BP predictions were returned. The top predictions by probability are listed below, with known annotations marked to distinguish established knowledge from novel predictions.

Top 10 GO-BP functional predictions for GPHRA from the GSFM model (gsfm-rummagene), ranked by predicted probability (Source: GSFM)

Rank	Term	Prob	Z	Known
1	protein transport (GO:0015031)	0.4608	1.88	Yes
2	intracellular protein transport (GO:0006886)	0.2690	2.03	No
3	Golgi organization (GO:0007030)	0.0352	1.85	No
4	G protein-coupled receptor signaling pathway (GO:0007186)	0.0330	1.68	No
5	regulation of protein stability (GO:0031647)	0.0265	1.79	No
6	monoatomic anion transmembrane transport (GO:0098656)	0.0255	1.97	Yes
7	endoplasmic reticulum to Golgi vesicle-mediated transport (G	0.0249	1.71	No
8	cellular response to osmotic stress (GO:0071470)	0.0112	1.94	No
9	mitochondrial calcium ion homeostasis (GO:0051560)	0.0108	1.94	No
10	multi-pass transmembrane protein insertion into ER membrane	0.0093	2.31	No

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 GPHRA. 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 datasets⁶.

A total of 2345 gene-set associations were found for GPHRA across 2 distinct datasets.

Data sources: JASPAR Predicted Human Transcription Factor Targets 2025, JASPAR Predicted Mouse Transcription Factor Targets 2025

GPHRA -- Top Harmonizome associations (Source: Harmonizome)

Rank	Gene Set	Z-Score
1	TFAP2A	+181.904
2	ZNF460	+157.011
3	ZNF740	+136.915
4	TFAP2C	+133.091
5	RREB1	+121.636
6	KLF15	+115.614
7	SP5	+87.376
8	FEZF2	+83.328
9	SREBF2	+81.745
10	POU2F1::SOX2	+76.057

Gene Annotation Lookup

The Enrichr genemap service was used to identify all curated gene sets and pathway terms that include GPHRA. 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:

GPHRA -- GO Biological Process terms containing this gene

Term
intracellular pH reduction (GO:0051452)
lymphocyte differentiation (GO:0030098)
positive regulation of I-kappaB kinase/NF-kappaB signaling (GO:0043123)
positive regulation of intracellular signal transduction (GO:1902533)
regulation of I-kappaB kinase/NF-kappaB signaling (GO:0043122)

ChEA Transcription Factor terms:

GPHRA -- ChEA TF targets containing this gene

Term
GATA6 25053715 ChIP-Seq YYC3 Human
CEBPA 26348894 ChIP-Seq LIVER Mouse
MYC 20876797 ChIP-ChIP MEDULLOBLASTOMA Human
TOP2B 26459242 ChIP-Seq MCF7 Human
FOXP1 21924763 ChIP-Seq HESC Human

Discussion

The literature footprint of GPHRA consists of 4 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, GPHRA represents a potentially druggable target. GPCRs are the largest and most successful class of drug targets, with approximately 35\% of FDA-approved drugs acting on GPCR targets. However, the orphan status of GPHRA -- its endogenous ligand remains unknown -- complicates therapeutic development. Ligand discovery efforts for orphan GPCRs typically involve high-throughput screening, reverse pharmacology approaches, or structure-based virtual screening once a reliable structural model is available.

Coexpression Insights

Coexpression analysis reveals that GPHRA shares expression patterns with GPR89B, ALG8, NOL10, 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 GPHRA 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 SNORA11B as the most literature-connected neighbor, though similarity scores were low across the board, reflecting the sparse annotation landscape around GPHRA.

Knowledge Gaps and Priorities

Key priorities for future investigation of GPHRA include:

1. Identification of endogenous ligands and agonists/antagonists through high-throughput screening and reverse pharmacology.
2. Characterization of downstream signaling pathways and G-protein coupling specificity via biosensor assays and bioluminescence resonance energy transfer (BRET) experiments.
3. Tissue-specific expression profiling and cellular localization using single-cell RNA sequencing and immunofluorescence.
4. Genetic association studies to link GPHRA 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} within broader pathophysiological networks.

Supplementary Information

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

GSFM predictions for GPHRA: https://gsfm.maayanlab.cloud/gene/GPR89A

Data Summary

Key identifiers and annotation counts for GPHRA are summarized below.

Data summary for GPHRA

Field	Value
Symbol	GPHRA
Entrez Gene ID	653519
Ensembl	ENSG00000117262
IDG classification	darkionchannel, ionchannel
PubMed co-mentions	4

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 ↩³

2. Weese, Michael and others. (2016). The MyGene.info information system: gene query, annotation and analysis on the fly. Nucleic Acids Research. ↩ ↩²

3. Wang, Zhong and others. (2020). Geneshot: a web-based platform for the analysis of gene sets. Nucleic Acids Research. ↩ ↩² ↩³ ↩⁴

4. Luo, Jun and others. (2025). Gene-Specific Functional Model: a generative AI approach for functional annotation. bioRxiv. ↩ ↩²

5. Rehfuss, Matthew and others. (2023). Enrichr: a cloud-based platform for genomic data analysis. Nucleic Acids Research. ↩ ↩²

6. Liberles, Daniel A and others. (2020). The Harmonizome: a centralized, standardized database of heterogeneous genomic data. Nucleic Acids Research. ↩ ↩²