ADRM1 Human

What is ADRM1 and what is its fundamental molecular function?

ADRM1 (Adhesion Regulating Molecule 1) is a protein-coding gene in humans that encodes a member of the adhesion regulating molecule 1 protein family. The encoded protein functions primarily as a component of the 26S proteasome, where it acts as a ubiquitin receptor and recruits the deubiquitinating enzyme, ubiquitin carboxyl-terminal hydrolase L5 (UCHL5) . ADRM1 is also known by several synonyms including ARM-1, ARM1, and GP110 .

At the molecular level, ADRM1 (also known as hRpn13) recognizes K48-linked polyubiquitinated proteins and facilitates their disassembly via deubiquitinating enzyme UCHL5, which is a critical step allowing for protein degradation via 20S proteasomal catalytic activities . ADRM1 is located within the 19S regulatory particle of the 26S proteasome, distinguishing it from the 20S proteolytic core particle that is commonly targeted by proteasome inhibitors .

Interestingly, while ADRM1 is primarily an intracellular protein involved in protein degradation, increased levels of ADRM1 have been associated with enhanced cell adhesion, though this is likely an indirect effect of its primary function .

How is ADRM1 gene expression regulated in normal tissues versus pathological states?

The dysregulation of ADRM1 has been implicated in carcinogenesis through various mechanisms . Research methodologies to study this dysregulation typically involve:

• Transcriptomic analysis using RNA sequencing or microarray data from repositories such as TCGA and Gene Expression Omnibus (GEO)

• Protein expression analysis via immunohistochemistry (IHC) with appropriate scoring systems (e.g., IHC scores based on percentage of staining positive cells: 0%, 1-10%, 11-50%, >50%)

• Comparative analysis between tumor and adjacent normal tissues using paired and unpaired statistical approaches

For researchers studying ADRM1 regulation, it is recommended to employ multiple validation datasets and conduct both bioinformatic analyses and experimental validations using patient samples.

What experimental approaches are most effective for studying ADRM1 protein interactions?

To effectively study ADRM1 protein interactions, researchers should consider multiple complementary approaches:

• Bioinformatic prediction methods: Tools like GeneMANIA can identify potential protein associations. Analysis shows ADRM1 protein is associated with specific proteins such as UBB, UBC, NOS2, and PSMA8 .

• Co-immunoprecipitation (Co-IP): For validating direct protein-protein interactions, particularly with UCHL5 and ubiquitinated substrates.

• Proteomics with tandem mass spectrometry: This approach has been used to identify ADRM1-modulated protein substrates and downstream signaling. For example, in ADRM1 knockout models, researchers identified 206 significantly downregulated proteins and 65 significantly upregulated proteins .

• Gene knockout/knockdown models: CRISPR-Cas9 gene editing to create ADRM1 knockout cell lines can help identify proteins that accumulate in the absence of ADRM1, suggesting they are direct substrates. Validation studies have confirmed several upregulated proteins in ADRM1-KO vs. wild-type cells, including SLC1A3, THBS1, GLYR1, GCLM, and TIMM8A .

• Proximity labeling techniques: BioID or APEX approaches can identify proteins in close proximity to ADRM1 within the cellular environment.

When designing these experiments, researchers should include appropriate controls and consider the complex nature of the 26S proteasome architecture and dynamics.

How does ADRM1 expression correlate with cancer prognosis and treatment response?

ADRM1 expression has emerged as a significant prognostic indicator in multiple cancer types, particularly well-documented in bladder cancer. The prognostic significance is supported by several lines of evidence:

Prognostic significance in bladder cancer:

• WHO high grade tumors (p = 0.01)

• T3-4 stage tumors (p = 0.01)

• N1-3 stage tumors (p = 0.036)

• Pathological stage III-IV (p = 0.004)

• Male patients (p = 0.05)

• Patients below 70 years old (p = 0.033)

Regarding treatment response, ADRM1 expression has predictive potential for both immunotherapy and chemotherapy responses:

• Immunotherapy response: High ADRM1 expression correlates with elevated expression of immune checkpoints including CD274 (PD-L1), CTLA4, PDCD1 (PD-1), and PDCD1LG2 (PD-L2), suggesting potential responsiveness to immune checkpoint inhibitors .

• Chemotherapy sensitivity: Patients with low ADRM1 expression showed greater sensitivity to cisplatin, docetaxel, vinblastine, mitomycin C, and methotrexate, as determined through IC50 analyses .

• Proteasome inhibitor resistance: In multiple myeloma, blockade of hRpn13/ADRM1 has been shown to trigger accumulation of polyubiquitinated proteins without affecting 20S proteasomal activities, inhibit MM cell growth, and importantly, overcome proteasome inhibitor resistance .

These findings suggest that ADRM1 expression assessment could be valuable for treatment stratification and as a potential therapeutic target.

What is the relationship between ADRM1 and the tumor immune microenvironment?

ADRM1 has significant associations with the tumor immune microenvironment, as evidenced by multiple analytical approaches:

• Gene Set Enrichment Analysis (GSEA) reveals that ADRM1 differentially expressed genes are enriched in immune-related categories, including:

  • Interleukin-1 signaling

  • Interleukin-1 family signaling

  • PD1 signaling

  • Interleukin-12 family signaling pathways

• Immune checkpoint correlation: In high ADRM1 expression groups, there is increased expression of critical immune checkpoints:

  • CD274 (PD-L1)

  • CTLA4

  • PDCD1 (PD-1)

  • PDCD1LG2 (PD-L2)

• Immune cell infiltration analysis: Using both CIBERSORT and TIP (Tracking Tumor Immunophenotype) methodologies, samples with high ADRM1 expression showed:

  • Significantly higher proportion of CD8+ T cells

  • Increased M1 Macrophage infiltration

  • Augmented proportion of CD8+ T naïve cells and CD4+ memory cells

  • Comparatively lower abundance of B plasma cells, CD4+ T memory resting cells, and activated Mast cells

• Tumor Mutation Burden (TMB): Patients in the high ADRM1 expression group had significantly higher TMB scores than those in the low ADRM1 expression group (p = 0.039), suggesting potentially greater neoantigen presentation and immunogenicity .

• Stemness characteristics: High ADRM1 expression is associated with higher mRNA expression-based stemness index (mRNAsi) scores (p < 0.001), which may impact immunotherapy response .

For researchers studying this relationship, methodological approaches should include immune deconvolution algorithms (e.g., CIBERSORT, TIP), correlation analyses with established immune markers, and functional validation in appropriate model systems.

What methodological approaches can be used to target ADRM1 therapeutically?

Several methodological approaches can be employed to target ADRM1 therapeutically:

• Small molecule inhibitors: Developing compounds that specifically bind to ADRM1's ubiquitin-binding domain or disrupt its interaction with UCHL5. This approach has shown promise in multiple myeloma, where blockade of hRpn13/ADRM1 triggers accumulation of polyubiquitinated proteins without affecting 20S proteasomal activities, inhibits cell growth, and overcomes proteasome inhibitor resistance .

• PROTAC (Proteolysis Targeting Chimera) technology: Creating bifunctional molecules that bind to ADRM1 and recruit E3 ubiquitin ligases to promote ADRM1 degradation.

• Gene silencing approaches: Using siRNA, shRNA, or antisense oligonucleotides to reduce ADRM1 expression. This approach requires optimization of delivery systems for in vivo applications.

• CRISPR-based gene editing: For research purposes, CRISPR-Cas9 has been used to create ADRM1 knockout models to study downstream effects. Therapeutic applications would require addressing delivery challenges and off-target effects.

• Combination therapies: Targeting ADRM1 in combination with established therapies. For instance:

  • In bladder cancer, combining ADRM1 inhibition with immune checkpoint inhibitors may be synergistic given the correlation between ADRM1 and immune checkpoint expression .

  • In multiple myeloma, combining ADRM1 inhibition with proteasome inhibitors might overcome resistance mechanisms .

When designing these approaches, researchers should consider:

• Target specificity to minimize off-target effects

• Pharmacokinetic and pharmacodynamic properties

• Biomarkers for patient selection (e.g., high ADRM1 expression)

• Appropriate model systems for preclinical validation

How can researchers quantify and assess the impact of ADRM1 on proteasome inhibitor resistance?

To quantify and assess ADRM1's impact on proteasome inhibitor resistance, researchers can employ several methodological approaches:

• Gene expression modulation:

  • Create isogenic cell lines with ADRM1 overexpression or knockdown/knockout

  • Use doxycycline-inducible systems for controlled expression

  • Employ CRISPR-Cas9 technology for precise genetic manipulation

• Cell viability and cytotoxicity assays:

  • Determine IC50 values for proteasome inhibitors in cells with varying ADRM1 expression

  • Assess dose-response curves and calculate resistance indices

  • Perform long-term clonogenic survival assays to evaluate acquired resistance

• Proteasome activity assays:

  • Measure 20S proteasomal catalytic activities (chymotrypsin-like, trypsin-like, and caspase-like) in the presence or absence of ADRM1

  • Assess accumulation of polyubiquitinated proteins via western blotting

  • Quantify protein substrate degradation rates

• Patient-derived models:

  • Analyze ADRM1 expression in patient samples before and after development of resistance

  • Utilize patient-derived xenografts or organoids to validate findings in more complex systems

  • Correlate ADRM1 expression with clinical response data

• Multiplexed proteomics:

  • Apply tandem mass spectrometry to identify ADRM1-modulated protein substrates

  • Use Gene Ontology enrichment and pathway analysis (e.g., Reactome) to delineate affected biological pathways

  • Validate key proteins by immunoblotting

In a validated approach, researchers have shown that blockade of hRpn13/ADRM1 triggers accumulation of polyubiquitinated proteins without affecting 20S proteasomal activities, inhibits multiple myeloma cell growth, and overcomes proteasome inhibitor resistance .

What is the functional significance of ADRM1 in cell adhesion and extracellular matrix interactions?

Despite its name as an adhesion regulating molecule, ADRM1's role in cell adhesion appears to be indirect and complex. Research methodologies to study this function include:

• Proteomic analysis: ADRM1/hRpn13 deletion studies have shown altered expression of numerous proteins related to adhesion and extracellular matrix (ECM) interactions, including:

  • Integrins: ITGA2, ITGA3, ITGB1, ITGB4

  • Laminins: LAMA3, LAMB2, LAMC2

  • Cell adhesion molecules: ALCAM, CNTN1, GLG1, HLA-B, ITGB1, L1CAM, SELPLG, VCAN

• Adhesion assays: Researchers can quantify cell-cell and cell-matrix adhesion in models with modulated ADRM1 expression using:

  • Static adhesion assays to various substrates (fibronectin, collagen, laminin)

  • Flow-based adhesion assays to measure adhesion under shear stress

  • 3D spheroid formation assays

• Migration and invasion assays: Since adhesion dynamics are critical for cell motility, assessment of:

  • Wound healing assays

  • Transwell migration assays

  • Matrix invasion assays

  • Real-time cell analysis systems

• Gene expression analysis: Correlation studies between ADRM1 and adhesion-related gene expression in patient samples and cell lines.

• Functional validation: siRNA or CRISPR-based approaches to modulate ADRM1 expression followed by assessment of adhesion phenotypes.

While increased levels of ADRM1 have been associated with increased cell adhesion , it's important to recognize that as an intracellular proteasome component, these effects are likely indirect through regulation of other adhesion-related proteins. This highlights the importance of comprehensive proteomic approaches to delineate the complete mechanistic pathway.

How can multi-omics approaches enhance our understanding of ADRM1 biology?

Integrating multiple omics technologies can provide a more comprehensive understanding of ADRM1 biology:

• Integrative genomic and proteomic analysis: Combining genomic data (e.g., mutations, copy number variations) with proteomic data (e.g., expression levels, post-translational modifications) can reveal how genetic alterations affect ADRM1 function.

• Transcriptomics and proteomics correlation: Researchers can use RNA-seq data alongside proteomic analyses to identify discordances between mRNA and protein levels, suggesting post-transcriptional regulation.

• Phosphoproteomics: Characterizing the phosphorylation status of ADRM1 and its interacting partners can reveal regulatory mechanisms and signaling pathways.

• Ubiquitinomics: Given ADRM1's role as a ubiquitin receptor, profiling the ubiquitinated proteome in the presence or absence of ADRM1 can identify specific substrates and degradation pathways.

• Metabolomics: Examining metabolic changes associated with ADRM1 modulation can uncover downstream functional consequences beyond protein degradation.

Advanced computational methods for data integration are essential, including:

• Network analysis algorithms

• Machine learning approaches for pattern recognition

• Pathway enrichment analyses that incorporate multiple data types

• Systems biology modeling of proteasome function

What are the most significant challenges in developing ADRM1-targeted therapies?

Developing ADRM1-targeted therapies presents several methodological and biological challenges:

Research strategies to address these challenges include:

• Structure-based drug design targeting specific ADRM1 domains

• Development of proteolysis-targeting chimeras (PROTACs) for selective ADRM1 degradation

• Combination strategies with established therapies

• Comprehensive biomarker development and validation

How does ADRM1 expression impact stemness and differentiation in cancer?

ADRM1 has emerging roles in cancer stemness and differentiation, with significant methodological considerations for researchers:

• Stemness index correlation: Studies have revealed a significant correlation between ADRM1 expression and mRNA expression-based stemness index (mRNAsi). Specifically, the high ADRM1 expression group exhibited higher stemness than the low ADRM1 expression group (p < 0.001) .

• Research methodologies to investigate this relationship include:

  • Analysis of stemness markers (e.g., SOX2, OCT4, NANOG) in models with modulated ADRM1 expression

  • Sphere formation assays to assess self-renewal capacity

  • Lineage tracing experiments to track differentiation patterns

  • Patient-derived organoid models to study stemness in a more physiologically relevant context

  • Single-cell RNA sequencing to identify stem-like subpopulations and their relationship to ADRM1 expression

• Therapeutic implications: Cellular stemness significantly influences response to both chemotherapy and immunotherapy . The association between ADRM1 and stemness suggests that ADRM1-targeted therapies might affect cancer stem cell populations.

• Methodological challenges:

  • Heterogeneity of cancer stem cell populations

  • Technical limitations in isolating and characterizing stem-like cells

  • Variability in stemness markers across different cancer types

  • Distinguishing correlation from causation in ADRM1-stemness relationships

Researchers should employ multiple complementary approaches and appropriate controls when investigating ADRM1's role in stemness, particularly focusing on the mechanistic connection between proteasome function and stem cell maintenance.

What are the most promising directions for ADRM1 research in the next five years?

Based on current findings and emerging trends, several promising research directions for ADRM1 include:

• Therapeutic development: Given ADRM1's role in proteasome inhibitor resistance in multiple myeloma and its prognostic significance in bladder cancer , developing selective ADRM1 inhibitors represents a promising avenue. These could be particularly valuable for patients who have developed resistance to conventional proteasome inhibitors.

• Biomarker validation: Further validating ADRM1 as a predictive biomarker for treatment response, particularly for immunotherapy given its correlations with immune checkpoints and the tumor immune microenvironment .

• Combination therapy approaches: Investigating synergistic effects between ADRM1 inhibition and other therapeutic modalities, such as immune checkpoint inhibitors or conventional chemotherapies.

• Substrate specificity: More comprehensive characterization of the specific protein substrates regulated by ADRM1, potentially identifying critical downstream targets for therapeutic intervention.

• Structural biology approaches: Detailed structural studies of ADRM1 interactions with ubiquitinated substrates and UCHL5 to inform structure-based drug design.

• Pan-cancer analysis: Expanding investigations of ADRM1's prognostic and predictive value across multiple cancer types beyond bladder cancer and multiple myeloma.

For researchers entering this field, employing integrated multi-omics approaches and collaborating across disciplines (structural biology, immunology, cancer biology, and drug development) will likely yield the most significant advances.

How can researchers establish causality between ADRM1 expression and clinical outcomes?

Establishing causality between ADRM1 expression and clinical outcomes requires rigorous methodological approaches:

• Prospective clinical studies: Design and implement prospective studies measuring ADRM1 expression (at protein and mRNA levels) in patient samples before treatment and correlate with treatment response and survival outcomes.

• Mechanistic studies: Utilize cellular and animal models with modulated ADRM1 expression to demonstrate direct effects on:

  • Tumor growth and metastasis

  • Response to therapies

  • Immune infiltration and activity

  • Stemness and differentiation

• Genetic approaches:

  • CRISPR-based screens to establish necessity and sufficiency of ADRM1 for specific phenotypes

  • Inducible expression systems to demonstrate temporal relationships

  • Rescue experiments to confirm specificity of observed effects

• Advanced in vivo models:

  • Patient-derived xenografts with modulated ADRM1 expression

  • Genetically engineered mouse models with tissue-specific ADRM1 alterations

  • Humanized immune system models to study ADRM1's effects on tumor-immune interactions

• Mediator analysis: Identify and validate downstream mediators of ADRM1's effects through:

  • Proteome-wide analysis of changes following ADRM1 modulation

  • Validation of key mediators through targeted approaches

  • Sequential intervention studies (e.g., ADRM1 inhibition followed by mediator modulation)