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A Multi-Source Profile of ASIC4: acid sensing ion channel subunit family member 4 as an IDG Understudied Ion Channel

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## Abstract

ASIC4 (acid sensing ion channel subunit family member 4) 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 64 PubMed co-mentions, identifying top coexpressed partners including STMN4, ATCAY, ELAVL3. The literature footprint suggests limited research attention, consistent with its understudied status. Enrichr gene-set lookup identifies inorganic cation transmembrane transport (GO:0098662) as a leading annotation. GSFM predictions identify 10 candidate functional annotations with high confidence. Regulatory element mapping via the CFDE Linked Data Hub reveals 266 regulatory elements and 239 xQTL evidence entries linked to the ASIC4 locus. Multi-dataset association analysis via the Harmonizome identifies 3467 gene-set associations across multiple biological datasets.Together, these findings provide a comprehensive baseline for future investigation of ASIC4 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]

ASIC4 is encoded by a gene with Entrez ID 55515 (Ensembl: ENSG00000072182). According to MyGeneInfo[^2]: This gene belongs to the superfamily of acid-sensing ion channels, which are proton-gated, amiloride-sensitive sodium channels. These channels have been implicated in synaptic transmission, pain perception as well as mechanoperception. This gene is predominantly expressed in the pituitary gland, and was considered a candidate for paroxysmal dystonic choreoathetosis (PDC), a movement disorder, however, no correlation was found between mutations in this gene and PDC. [provided by RefSeq, Feb 2012].

## 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 64 PubMed IDs mention ASIC4 in GeneRIF annotations[^3].

Top co-mentioned genes with ASIC4:

*ASIC4 -- Top co-mentioned genes*
| Rank | Gene | PMIDs | Fraction |
| --- | --- | --- | --- |
| 1 | 5\_8S\_rRNA | 2 | 0.0003 |
| 2 | ASIC1 | 2 | 0.0227 |
| 3 | ASIC4 | 2 | 0.1818 |
| 4 | MIR7150 | 2 | 0.0513 |
| 5 | ASIC2 | 1 | 0.0049 |
| 6 | ASIC3 | 1 | 0.0064 |
| 7 | TRPM3 | 1 | 0.0192 |
| 8 | TRPV3 | 1 | 0.0145 |

### Coexpression Network

ARCHS4-based coexpression analysis identifies genes whose expression patterns correlate with ASIC4 across human tissues, suggesting shared biological functions or co-regulation[^3].

A total of 20 genes were identified as significantly coexpressed with ASIC4. The top partners by similarity score are listed below.

*Top 10 genes coexpressed with ASIC4 across human tissues, ranked by similarity score (Source: ARCHS4)*
| Rank | Gene | Similarity |
| --- | --- | --- |
| 1 | STMN4 | 0.5347 |
| 2 | ATCAY | 0.5255 |
| 3 | ELAVL3 | 0.5252 |
| 4 | AP3B2 | 0.5206 |
| 5 | FAM57B | 0.5159 |
| 6 | AMER2 | 0.5126 |
| 7 | CELF5 | 0.4998 |
| 8 | SCRT1 | 0.4982 |
| 9 | TNR | 0.4975 |
| 10 | ACTL6B | 0.4926 |

![Bar chart showing the top coexpressed genes for ASIC4, with similarity scores derived from ARCHS4 cross-tissue correlation analysis. (Source: ARCHS4 via Geneshot)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/ASIC4/coexpression_bar.png)

### Literature-Based Gene Similarity

GeneRIF-based similarity identifies genes that share biological context with ASIC4 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 ASIC4, ranked by GeneRIF similarity score*
| Rank | Gene | Similarity |
| --- | --- | --- |
| 1 | ASIC1 | 0.0119 |
| 2 | PER2 | 0.0000 |
| 3 | PER3 | 0.0000 |
| 4 | PERM1 | 0.0000 |
| 5 | PERP | 0.0000 |
| 6 | PES1 | 0.0000 |
| 7 | PET100 | 0.0000 |
| 8 | PFDN1 | 0.0000 |
| 9 | PFDN2 | 0.0000 |
| 10 | PFDN4 | 0.0000 |

### 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 ASIC4. Regulatory elements include enhancers, promoters, and insulators whose activity profiles overlap or target the ASIC4 locus.

A total of **266** regulatory elements overlap or are linked to the ASIC4 locus, with **239** xQTL evidence entries providing variant-to-expression associations. This level of regulatory annotation suggests that the ASIC4 locus is subject to complex, tissue-specific regulatory control.

![Summary of regulatory elements and xQTL evidence linked to the ASIC4 locus, as reported by the CFDE Linked Data Hub. (Source: CFDE Linked Data Hub)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/ASIC4/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 ASIC4, 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 ASIC4 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/ASIC4/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 ASIC4, 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 ASIC4 from the GSFM model (gsfm-rummagene), ranked by predicted probability (Source: GSFM)*
| Rank | Term | Prob | Z | Known |
| --- | --- | --- | --- | --- |
| 1 | monoatomic ion transport (GO:0006811) | 0.8366 | 2.22 | Yes |
| 2 | monoatomic ion transmembrane transport (GO:0034220) | 0.8364 | 2.51 | Yes |
| 3 | transmembrane transport (GO:0055085) | 0.5862 | 2.19 | No |
| 4 | calcium ion transmembrane transport (GO:0070588) | 0.3202 | 2.27 | No |
| 5 | potassium ion transport (GO:0006813) | 0.2536 | 2.43 | No |
| 6 | monoatomic cation transmembrane transport (GO:0098655) | 0.1379 | 2.48 | No |
| 7 | chloride transmembrane transport (GO:1902476) | 0.1364 | 2.48 | No |
| 8 | calcium ion transport (GO:0006816) | 0.1126 | 2.03 | No |
| 9 | potassium ion transmembrane transport (GO:0071805) | 0.0970 | 2.16 | No |
| 10 | sodium ion transmembrane transport (GO:0035725) | 0.0549 | 1.98 | Yes |

![Hierarchical bar chart of GSFM GO-BP predictions for ASIC4, showing predicted functional terms ranked by probability with z-score shading. (Source: GSFM, Ma'ayan Lab)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/ASIC4/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 ASIC4. 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 **3467** gene-set associations were found for ASIC4 across 2 distinct datasets.

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

*ASIC4 -- Top Harmonizome associations (Source: Harmonizome)*
| Rank | Gene Set | Z-Score |
| --- | --- | --- |
| 1 | ZNF740 | +248.278 |
| 2 | ZNF148 | +175.222 |
| 3 | KLF5 | +132.050 |
| 4 | KLF4 | +105.756 |
| 5 | MAZ | +99.559 |
| 6 | KLF16 | +97.703 |
| 7 | ZNF816 | +77.919 |
| 8 | KLF17 | +75.874 |
| 9 | SP9 | +71.445 |
| 10 | KLF2 | +69.217 |

![Top 10 Harmonizome gene-set associations for ASIC4, ranked by standardized z-score across multiple curated biological datasets. Data: JASPAR TF Target predictions. (Source: Harmonizome)](https://s3.k8s.maayanlab.cloud/autoxiv/bio/ASIC4/harmonizome_associations.png)

### Gene Annotation Lookup

The Enrichr genemap service was used to identify all curated gene sets and pathway terms that include ASIC4. 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:**

*ASIC4 -- GO Biological Process terms containing this gene*
| Term |
| --- |
| inorganic cation transmembrane transport (GO:0098662) |
| sodium ion transmembrane transport (GO:0035725) |
| sodium ion transport (GO:0006814) |

**KEGG Pathway terms:**

*ASIC4 -- KEGG pathways containing this gene*
| Pathway |
| --- |
| Inflammatory mediator regulation of TRP channels |

**ChEA Transcription Factor terms:**

*ASIC4 -- ChEA TF targets containing this gene*
| Term |
| --- |
| GATA2 CHEA |
| GATA1 CHEA |

**GWAS Catalog associations:**

*ASIC4 -- GWAS Catalog entries containing this gene*
| Trait |
| --- |
| Job labourers in process/plant operations @22617 9139 |
| Right 6mm regularity index @5161 raw |
| Self-reported low platelets/platelet disorder @20002 1327 |

## Discussion

The literature footprint of ASIC4 consists of 64 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, ASIC4 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 ASIC4 -- 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 ASIC4 shares expression patterns with STMN4, ATCAY, ELAVL3, 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 ASIC4 activity. Given GPCRs often operate in complex signaling cascades, coexpressedproviding partnersclues couldabout includeits G-proteinbiological subunits,context downstreamand effectors,potential ortherapeutic neighboring receptors in shared pathwaysrelevance.

### Literature Network Insights

GeneRIF-based similarity identifies ASIC1 as the most literature-connected neighbor, though similarity scores were low across the board, reflecting the sparse annotation landscape around ASIC4.

### Knowledge Gaps and Priorities

Key priorities for future investigation of ASIC4 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 ASIC4 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}ASIC4 within broader pathophysiological networks.

## Supplementary Information

This section provides quick-reference summaries and resource links for ASIC4. The GSFM model page can be revisited for up-to-date functional predictions as the model is retrained:

GSFM predictions for ASIC4: [https://gsfm.maayanlab.cloud/gene/ASIC4](https://gsfm.maayanlab.cloud/gene/ASIC4)

### Data Summary

1Key identifiers and annotation counts for ASIC4 are summarized below.

*Data summary for ASIC4**
| Field | Value |
| --- | --- |
| Symbol**: | ASIC4 |
2.| **Entrez Gene ID**: | 55515 |
3.| **Ensembl**: | ENSG00000072182 |
4.| **IDG classification**: | darkionchannel, ionchannel |
5.| **PubMed co-mentions**: | 64 |

[^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.