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

Changes in v2:Fixed nan simScore values: filtered coexpression and literature similarity tables, omitted coexpression section when no valid data, fixed abstract text.

+208 / −919 words
## Abstract

GPHRB (golgi pH regulator B) is a gene classified within the Illuminating the Druggable Genome (IDG) program as a darkionchannel   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 60 PubMed co-mentions, identifying top coexpressed partners including GPR89A, ALG6, CBWD2none. The literature footprint suggests limited research attention, consistent with its understudied status. Enrichr gene-set lookup identifies intracellularCholesterol pHHomeostasis reduction (GO:00514520042632) as a leading annotation. No GSFM predictions identify 10 candidate functional annotationspredictions withwere high confidenceavailable. No regulatory element data were available for this locus. Multi-dataset association analysis via the Harmonizome identifies 23451357 gene-set associations across multiple biological datasets.Together, these findings provide a comprehensive baseline for future investigation of GPHRB 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]

GPHRB is encoded by a gene with Entrez ID 51463 (Ensembl: ENSG00000117262). According to MyGeneInfo[^2]: Enables voltage-gated anion channel activity. Involved in intracellular pH reduction. Located in Golgi cisterna membrane and Golgi-associated vesicle 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 60 PubMed IDs mention GPHRB in GeneRIF annotations[^3].

### Coexpression Network

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

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

*Top 10 genes coexpressed with GPHRB across human tissues, ranked by similarity score (Source: ARCHS4)*
| Rank | Gene | Similarity |
| --- | --- | --- |
| 1 | GPR89A | 0.5539 |
| 2 | ALG6 | 0.4474 |
| 3 | CBWD2 | 0.4331 |
| 4 | CBWD1 | 0.4194 |
| 5 | PMPCB | 0.4148 |
| 6 | MFSD8 | 0.4024 |
| 7 | MYNN | 0.4017 |
| 8 | NVL | 0.3983 |
| 9 | ORC5 | 0.3981 |
| 10 | POC5 | 0.3966 |

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

### Literature-Based Gene Similarity

GeneRIF-based similarity identifies genes that share biological context with GPHRB in curated gene-relation-inference statements compiled from the biomedical literature[^3].

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

*Top 10 literature-similar genes were identified for GPHRB, rankedconsistent bywith GeneRIFits similarityunderstudied score*
|status Rankand |sparse GeneGeneRIF | Similarity |
| --- | --- | --- |
| 1 | WTIP | 0annotation.2381 |
| 2 | SEC11C | 0.0909 |
| 3 | ASXL2 | 0.0370 |
| 4 | STXBP5 | 0.0164 |
| 5 | PPM1K-DT | 0.0127 |
| 6 | TAP1 | 0.0047 |
| 7 | AGTR1 | 0.0011 |
| 8 | KIR2DL4 | 0.0009 |
| 9 | RNA5-8SN4 | 0.0004 |
| 10 | RNA5-8SN1 | 0.0002 |

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

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

![Summary of regulatory elements and xQTL evidence linked to the GPHRB locus, as reported by the CFDE Linked Data Hub. (Source: CFDE Linked Data Hub)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/GPHRB/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 GPHRB, 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 GPHRB 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/GPHRB/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 GPHRB, returning predicted terms with associated probabilities, z-scores, and known-annotation status.

ANo totalGSFM 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 GPHRB from the GSFM model (gsfm-rummagene), ranked by predicted probability (Source: GSFM)*
| Rank | Term | Prob | Z | Known |
| --- | --- | --- | --- | --- |
| 1 | endoplasmic reticulum to Golgi vesicle-mediated transport (G | 0.2327 | 2.53 | No |
| 2 | Golgi organization (GO:0007030)\_Process | 0source.0711 | 2.13 | No |
| 3 | protein targeting to membrane (GO:0006612) | 0.0636 | 2.73 | No |
| 4 | T cell receptor signaling pathway (GO:0050852) | 0.0456 | 1.67 | No |
| 5 | protein localization to membrane (GO:0072657) | 0.0287 | 1.81 | No |
| 6 | monoatomic anion transmembrane transport (GO:0098656) | 0.0259 | 1.98 | Yes |
| 7 | T cell differentiation (GO:0030217) | 0.0255 | 1.71 | Yes |
| 8 | cellular response to osmotic stress (GO:0071470) | 0.0248 | 2.28 | No |
| 9 | intracellular pH reduction (GO:0051452) | 0.0243 | 2.32 | Yes |
| 10 | negative regulation of GTPase activity (GO:0034260) | 0.0199 | 2.35 | No |

![Hierarchical bar chart of GSFM GO-BP predictions for GPHRB, showing predicted functional terms ranked by probability with z-score shading. (Source: GSFM, Ma'ayan Lab)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/GPHRB/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 GPHRB. 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 **23451357** gene-set associations were found for GPHRB across 29 distinct datasets.

Data sources: JASPARAllen PredictedBrain Atlas Developing Human TranscriptionBrain FactorTissue TargetsGene 2025Expression Profiles by RNA-seq, JASPARAllen PredictedBrain MouseAtlas TranscriptionPrenatal FactorHuman TargetsBrain Tissue Gene Expression Profiles, CCLE Cell Line Gene CNV Profiles, CCLE Cell Line Proteomics, CM4AI KOLF21J CRISPRi Gene Perturbation Atlas, DISEASES Curated Gene-Disease Association Evidence Scores 2025, GEO Signatures of Differentially Expressed Genes for Kinase Perturbations, GTEx Tissue Sample Gene Expression Profiles, Klijn et al., Nat. Biotechnol., 2015 Cell Line Gene CNV Profiles

*GPHRB -- Top Harmonizome associations (Source: Harmonizome)*
| Rank | Gene Set | Z-Score |
| --- | --- | --- |
| 1 | TFAP2AEEF1G | +1818.904641 |
| 2 | ZNF460ISHIKAWAHERAKLIO02ER\_ENDOMETRIUM | +1576.011856 |
| 3 | ZNF740HCC44\_LUNG | +1366.915566 |
| 4 | TFAP2CSW1573\_LUNG | +1335.091940 |
| 5 | RREB1SKMEL30\_SKIN | +1215.636374 |
| 6 | KLF15NOB1 | +1155.614087 |
| 7 | SP5Genetic disease | +875.376000 |
| 8 | FEZF2Disease | +835.328000 |
| 9 | SREBF2Chromosomal disease | +815.745000 |
| 10 | POU2F1::SOX2Chromosomal deletion syndrome | +765.057000 |

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

### Gene Annotation Lookup

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

*GPHRB -- GO Biological Process terms containing this gene*
| Term |
| --- |
| intracellularCholesterol pHHomeostasis reduction (GO:00514520042632) |
| lymphocyteIntracellular differentiationpH Reduction (GO:00300980051452) |
| regulationLymphocyte ofDifferentiation intracellular pH (GO:00514530030098) |
| TSterol cellHomeostasis activation (GO:00421100055092) |
| T cellCell differentiationActivation (GO:00302170042110) |

**ChEA Transcription Factor terms:**

*GPHRB -- ChEA TF targets containing this gene*
| Term |
| --- |
| PADI4 21655091 ChIP-ChIP MCF7 Human |
| RUNX2 22187159 ChIP-Seq PCA Human |
| TP63 22573176 ChIP-Seq HFKS Human |
| XRN2 22483619 ChIP-Seq HELA Human |

## Discussion

The literature footprint of GPHRB consists of 60 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 protein family family, GPHRB 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 GPHRB -- 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 GPHRB shares expression patterns with GPR89A, ALG6, CBWD2, 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 GPHRB 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 WTIP as the most literature-connected neighbor, though similarity scores were low across the board, reflecting the sparse annotation landscape around GPHRB.

### Knowledge Gaps and Priorities

Key priorities for future investigation of GPHRB 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 GPHRB 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}GPHRB within broader pathophysiological networks.

## Supplementary Information

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

GSFM predictions for GPHRB: [https://gsfm.maayanlab.cloud/gene/GPR89BGPHRB](https://gsfm.maayanlab.cloud/gene/GPR89BGPHRB)

### Data Summary

Key identifiers and annotation counts for GPHRB are summarized below.

*Data summary for GPHRB*
| Field | Value |
| --- | --- |
| Symbol | GPHRB |
| Entrez Gene ID | 51463 |
| Ensembl | ENSG00000117262 |
| IDG classification | darkionchannel   |
| PubMed co-mentions | 60 |
## 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.