Recombinant Human Transmembrane protein 31 (TMEM31)

What is the basic cellular function of TMEM31?

TMEM31 is a transmembrane protein involved in intracellular trafficking and membrane dynamics. Current research indicates its role in cellular processes related to membrane organization and potential involvement in signaling pathways. Methodologically, researchers investigating TMEM31 function typically employ knockout or knockdown studies followed by phenotypic analysis of cellular processes such as vesicular transport, protein trafficking, and membrane organization. Immunofluorescence microscopy and co-immunoprecipitation are commonly used to identify TMEM31's subcellular localization and binding partners .

How is TMEM31 structurally characterized?

TMEM31 is characterized as a transmembrane protein with specific domains that contribute to its function. Based on current structural analyses, TMEM31 contains transmembrane regions that anchor it to cellular membranes. For researchers investigating TMEM31 structure, approaches include computational modeling of protein structure, membrane topology prediction algorithms, and experimental validation through techniques such as limited proteolysis combined with mass spectrometry. When expressing recombinant TMEM31, researchers should consider using mammalian expression systems that allow proper post-translational modifications and membrane insertion, as bacterial systems may not support the correct folding of this transmembrane protein .

Which cell types and tissues express TMEM31 under normal physiological conditions?

Under normal physiological conditions, TMEM31 shows a restricted expression pattern that is characteristic of cancer/testis antigens. According to data from the Human Protein Atlas, TMEM31 expression is primarily limited to testicular tissue in healthy individuals. This restricted normal tissue expression makes it an interesting target for cancer research. To verify TMEM31 expression in experimental models, researchers should employ RT-qPCR for mRNA detection, Western blot for protein expression using validated antibodies such as the TMEM31 Polyclonal Antibody (PACO30582), and immunohistochemistry with appropriate positive and negative tissue controls .

What is the expression pattern of TMEM31 across different cancer types?

TMEM31 shows significant expression across multiple cancer types, consistent with its classification as a potential cancer/testis antigen. According to the Human Protein Atlas database, TMEM31 expression and overexpression have been detected in a diverse range of malignancies including glioma, lung cancer, pancreatic cancer, colorectal cancer, urothelial cancer, prostate cancer, testis cancer, breast cancer, ovarian cancer, endometrial cancer, and melanoma . Of particular note, TMEM31 expression increases during metastatic melanoma progression, suggesting it as a potential biomarker and therapeutic target for advanced disease stages. Researchers investigating TMEM31 in cancer should employ tissue microarrays with multiple tumor types and matched normal tissues, using validated antibodies for immunohistochemical detection .

How can TMEM31 be utilized as a target for cancer vaccine development?

TMEM31's properties as a cancer/testis antigen make it a promising target for cancer vaccine development, particularly for melanoma. The methodological approach involves:

• Epitope identification: Using in silico analysis tools to identify immunodominant epitopes with high binding affinity to MHC complexes

• Construct design: Incorporating selected epitopes with appropriate linkers to facilitate processing and presentation

• Adjuvant selection: Including elements like Beta-defensin to activate TLR4-MD pathways

• Helper epitope integration: Adding PADRE peptide sequences or tetanus toxin fragment C (TTFrC) to activate helper T lymphocytes

Research indicates that the 32-62, 77-105, and 125-165 residues of TMEM31 may represent particularly immunodominant fragments that could be incorporated into multiepitope vaccines. These fragments should be linked with motifs like RVRR and HEYGAEALERAG to improve epitope separation and presentation .

What methods are recommended for validating TMEM31 as a biomarker in cancer progression?

To validate TMEM31 as a cancer biomarker, researchers should implement a multi-phase validation approach:

• Discovery phase: Analysis of TMEM31 expression in public databases (TCGA, GEO) across cancer types and stages

• Verification phase: RT-qPCR and Western blot analysis of TMEM31 expression in cell lines and patient-derived xenografts

• Validation phase: Immunohistochemical analysis of TMEM31 in tissue microarrays with statistical correlation to clinical outcomes

• Clinical utility assessment: Prospective studies correlating TMEM31 expression with treatment response and survival

For melanoma specifically, given the observed correlation between TMEM31 expression and metastatic progression, researchers should analyze paired primary and metastatic samples from the same patients to establish its utility as a progression marker .

What antibodies and detection methods are currently available for TMEM31 research?

Researchers have several validated antibodies and detection systems available for TMEM31 studies:

When selecting antibodies for TMEM31 detection, researchers should consider:

• Application compatibility: The PACO30582 antibody has been validated for ELISA, immunohistochemistry, and immunofluorescence applications

• Epitope recognition: This antibody was raised against recombinant human TMEM31 protein (amino acids 1-118)

• Storage and handling: The antibody is supplied in 50% glycerol with 0.03% Proclin 300 and 0.01M PBS at pH 7.4

For optimal results, include appropriate positive controls (tissues with known TMEM31 expression) and negative controls (tissues lacking TMEM31 expression) in experiments. The antibody has been specifically validated on human adrenal gland tissue and HepG2 cells .

What expression systems are most effective for producing recombinant TMEM31?

For producing recombinant TMEM31, researchers should consider the following expression systems based on the intended application:

• Mammalian expression systems (HEK293, CHO cells):

  • Advantages: Proper post-translational modifications and protein folding

  • Recommended for: Structural studies, functional assays, antibody production

  • Vectors: pCDNA3.1, pCAGGS with appropriate tags (His, FLAG) for purification

• Insect cell systems (Sf9, High Five):

  • Advantages: Higher yield than mammalian systems while maintaining eukaryotic processing

  • Recommended for: Protein production for biochemical assays

  • Vectors: Baculovirus expression vectors with polyhistidine tags

• Cell-free expression systems:

  • Advantages: Rapid production of transmembrane proteins with supplemented lipids

  • Recommended for: Initial screening and structural analysis

For transmembrane proteins like TMEM31, detergent screening is critical for solubilization while maintaining protein structure and function. Consider initial screening with mild detergents such as DDM, LMNG, or digitonin. Purification typically involves affinity chromatography followed by size exclusion chromatography to ensure sample homogeneity .

What are the recommended protocols for in silico analysis of TMEM31 epitopes?

For researchers conducting in silico analysis of TMEM31 epitopes, a systematic computational workflow is recommended:

• MHC binding prediction:

  • Tools: NetMHC, IEDB Analysis Resource, SYFPEITHI

  • Parameters: IC50 values <500 nM for strong binders

  • Allele coverage: Include common HLA-A, HLA-B, and HLA-DR alleles

• Epitope optimization:

  • Analyze epitope conservation across human populations

  • Check for post-translational modification sites that might interfere with processing

  • Verify absence of homology with self-antigens to prevent autoimmunity

• Construct design:

  • Link selected epitopes with cleavable linkers (e.g., RVRR, HEYGAEALERAG)

  • Include helper T cell epitopes like PADRE or TTFrC

  • Consider adding TLR-activating sequences like Beta-defensin

• Validation metrics:

  • Antigenicity and immunogenicity prediction

  • Population coverage analysis

  • Molecular dynamics simulation for construct stability

This computational approach has been successfully employed to identify the immunodominant fragments (residues 32-62, 77-105, and 125-165) of TMEM31 with high predicted immunogenicity for cancer vaccine development .

How does TMEM31 expression correlate with immune infiltration in the tumor microenvironment?

The relationship between TMEM31 expression and immune infiltration represents an important research question with therapeutic implications. Researchers investigating this correlation should:

• Perform multiplex immunohistochemistry or imaging mass cytometry to simultaneously visualize TMEM31 expression and immune cell populations (CD8+ T cells, NK cells, macrophages, etc.)

• Conduct gene correlation analyses using public databases to identify associations between TMEM31 expression and immune signature genes

• Analyze single-cell RNA sequencing data from tumor samples to characterize the immune phenotype of TMEM31-expressing cells and neighboring immune cells

• Evaluate the impact of TMEM31 expression on immune checkpoint molecule expression (PD-L1, CTLA-4) to determine potential synergies with immunotherapy

Current research suggests that cancer/testis antigens like TMEM31 may influence the immune landscape of tumors, potentially affecting response to immunotherapy. This represents an important direction for investigation, particularly in melanoma where immune checkpoint inhibitors are a standard treatment approach .

What mechanisms regulate TMEM31 expression in normal and malignant cells?

Understanding the regulatory mechanisms controlling TMEM31 expression provides insights into its restricted normal tissue expression and upregulation in cancer. Researchers should investigate:

• Epigenetic regulation:

  • DNA methylation analysis of the TMEM31 promoter region in normal versus cancer tissues

  • Histone modification profiling (H3K4me3, H3K27me3, H3K27ac) at the TMEM31 locus

  • Effects of DNA methyltransferase inhibitors (5-azacytidine) and histone deacetylase inhibitors on TMEM31 expression

• Transcriptional regulation:

  • Promoter analysis to identify binding sites for cancer-associated transcription factors

  • ChIP-seq experiments to confirm transcription factor binding

  • Reporter assays to validate functional regulatory elements

• Post-transcriptional regulation:

  • miRNA binding site prediction and validation

  • RNA stability assays in normal versus cancer cells

The restricted expression pattern characteristic of cancer/testis antigens like TMEM31 is typically maintained by epigenetic silencing in normal tissues, with aberrant demethylation occurring in cancer cells. This understanding can inform both diagnostic approaches and potential therapeutic strategies targeting the regulatory machinery .

What approaches can be used to analyze the functional impact of TMEM31 in cancer progression?

To determine the functional significance of TMEM31 in cancer progression, researchers should employ multiple complementary approaches:

• Loss-of-function studies:

  • CRISPR/Cas9-mediated knockout in cancer cell lines with high TMEM31 expression

  • shRNA or siRNA-mediated knockdown for transient suppression

  • Analysis of phenotypic changes in proliferation, migration, invasion, and resistance to apoptosis

• Gain-of-function studies:

  • Overexpression of TMEM31 in cell lines with low endogenous expression

  • Inducible expression systems to study temporal effects

  • Domain mutation studies to identify critical functional regions

• In vivo models:

  • Xenograft studies with TMEM31-modulated cell lines

  • Analysis of metastatic potential in tail vein injection or orthotopic models

  • Patient-derived xenografts with varying TMEM31 expression levels

• Mechanistic investigations:

  • Interactome analysis using BioID or IP-MS approaches

  • Signaling pathway analysis focusing on pathways relevant to cancer progression

  • Membrane proteome changes in response to TMEM31 modulation

Given the observed correlation between TMEM31 expression and melanoma progression, these functional studies are particularly relevant for understanding its potential role in metastasis and for validating it as a therapeutic target .

What are common challenges in expressing and purifying recombinant TMEM31?

Researchers working with recombinant TMEM31 often encounter several technical challenges:

• Low expression yield:

  • Solution: Optimize codon usage for the expression system

  • Test different promoters and signal sequences

  • Consider fusion tags that enhance expression (SUMO, MBP)

• Protein misfolding:

  • Solution: Lower induction temperature (16-20°C) and reduce inducer concentration

  • Include molecular chaperones in expression systems

  • Test different cell lines optimized for membrane protein expression

• Aggregation during purification:

  • Solution: Screen detergent panels systematically (starting with DDM, LMNG, GDN)

  • Include stabilizing agents like cholesterol or specific lipids

  • Optimize buffer conditions (pH, salt concentration, glycerol)

• Protein degradation:

  • Solution: Include protease inhibitors throughout purification

  • Maintain low temperature during all processing steps

  • Consider engineering constructs with stabilizing mutations

• Verification of proper folding:

  • Solution: Circular dichroism to verify secondary structure

  • Fluorescence-based thermal shift assays to assess stability

  • Limited proteolysis to confirm compact, folded domains

When producing recombinant TMEM31 for functional or structural studies, researchers should consider starting with truncated constructs focusing on specific domains if expression of the full-length protein proves challenging .

How can researchers address discrepancies in TMEM31 detection across different experimental platforms?

When facing inconsistent TMEM31 detection results across different experimental approaches, researchers should implement a systematic troubleshooting strategy:

• Antibody validation:

  • Verify antibody specificity using positive controls (known TMEM31-expressing samples)

  • Include knockout/knockdown controls to confirm specificity

  • Test multiple antibodies targeting different epitopes

  • Validate each antibody for the specific application (Western blot, IHC, IF)

• Expression analysis harmonization:

  • For mRNA detection, design primers spanning exon-exon junctions

  • Use absolute quantification with standard curves for RT-qPCR

  • Normalize to multiple reference genes selected for stability in the tissue type

• Sample preparation optimization:

  • For membrane proteins like TMEM31, ensure complete lysis (consider detergent combinations)

  • Optimize fixation conditions for IHC and IF applications

  • Prevent protein degradation through proper sample handling and storage

• Cross-platform validation:

  • Correlate protein detection with mRNA expression

  • Confirm subcellular localization through fractionation followed by Western blot

  • Employ orthogonal detection methods (mass spectrometry) for validation

When performing comparative studies, maintain consistent experimental conditions across all samples, particularly regarding fixation times, antibody concentrations, and detection parameters .

What considerations are important when designing TMEM31-targeted immunotherapies to avoid off-target effects?

Developing safe and effective TMEM31-targeted immunotherapies requires careful consideration of potential off-target effects:

• Comprehensive expression profiling:

  • Analyze TMEM31 expression across all normal human tissues using multiple databases

  • Conduct immunohistochemistry on tissue microarrays representing all major organs

  • Perform single-cell RNA sequencing to identify rare cell populations expressing TMEM31

• Epitope selection criteria:

  • Prioritize epitopes unique to TMEM31 without homology to other proteins

  • Conduct BLAST searches to identify potential cross-reactive peptides

  • Test candidate epitopes against T cells from healthy donors to assess self-reactivity

• Pre-clinical safety assessment:

  • Test against panels of normal human cells representing tissues of concern

  • Utilize humanized mouse models for in vivo safety assessment

  • Consider employing inducible or split systems with tumor-specific promoters

• Clinical translation considerations:

  • Implement dose-escalation studies with comprehensive safety monitoring

  • Develop biomarkers to assess on-target activity versus off-target toxicity

  • Prepare mitigation strategies for potential adverse events

How might TMEM31 interact with other membrane proteins in cancer signaling networks?

Understanding TMEM31's interaction with other membrane proteins represents an important frontier in cancer biology research. Investigators should consider:

• Protein-protein interaction screening:

  • Membrane yeast two-hybrid systems specifically designed for transmembrane proteins

  • Proximity labeling approaches (BioID, APEX) adapted for membrane environments

  • Co-immunoprecipitation using membrane-compatible detergents followed by mass spectrometry

• Functional validation of interactions:

  • FRET or BRET assays to confirm direct interactions in living cells

  • Split reporter systems (split-GFP, luciferase complementation) for dynamic interaction studies

  • Co-localization analysis using super-resolution microscopy techniques

• Signaling pathway analysis:

  • Phosphoproteomics before and after TMEM31 modulation

  • Lipidomics to assess membrane composition changes

  • Interactome changes in response to growth factors or stress conditions

• Computational modeling:

  • Protein docking simulations to predict interaction interfaces

  • Network analysis to place TMEM31 in the context of cancer signaling pathways

  • Molecular dynamics simulations of TMEM31 within lipid bilayers

These approaches will help elucidate whether TMEM31 functions autonomously or as part of larger signaling complexes in cancer cells, potentially revealing new vulnerabilities for therapeutic exploitation .

What role might TMEM31 play in resistance to current cancer therapies?

Investigating TMEM31's potential role in therapy resistance requires systematic approaches:

• Correlation studies:

  • Analysis of TMEM31 expression in paired samples before treatment and after resistance development

  • Mining public datasets for associations between TMEM31 expression and response to specific therapies

  • Single-cell analysis of residual disease after treatment to identify TMEM31-expressing subpopulations

• Functional studies:

  • Modulate TMEM31 expression (overexpression, knockdown) and assess changes in drug sensitivity

  • Investigate changes in resistance-associated pathways (apoptosis, DNA repair, drug efflux)

  • Examine TMEM31's impact on cancer stem cell properties and metabolic adaptations

• Combination approaches:

  • Test TMEM31-targeted therapies in combination with standard treatments

  • Evaluate potential synergistic effects with immune checkpoint inhibitors

  • Develop rational combination strategies based on mechanistic understanding

• Biomarker development:

  • Evaluate TMEM31 as a predictive biomarker for treatment response

  • Develop assays for monitoring TMEM31 levels during treatment

  • Correlate changes in TMEM31 expression with clinical outcomes

Given TMEM31's membrane localization and potential role in cellular signaling, it could influence drug uptake, efflux, or downstream signaling pathways relevant to therapy resistance .

How can computational approaches advance TMEM31 research toward clinical applications?

Computational methodologies offer powerful approaches to accelerate TMEM31 research toward clinical translation:

• Structure prediction and molecular modeling:

  • Apply AlphaFold2 or RoseTTAFold for accurate TMEM31 structure prediction

  • Conduct virtual screening against the predicted structure to identify small molecule binders

  • Simulate membrane insertion and dynamics using specialized MD force fields

• Epitope optimization for immunotherapy:

  • Employ machine learning algorithms to predict immunogenicity across diverse HLA types

  • Optimize epitope combinations for maximum population coverage

  • Model TCR-pMHC interactions to predict T cell response strength

• Integrative multi-omics analysis:

  • Integrate transcriptomics, proteomics, and clinical data to identify TMEM31-associated signatures

  • Apply network medicine approaches to position TMEM31 in disease-relevant pathways

  • Develop predictive models for patient stratification based on TMEM31-related signatures

• Clinical translation tools:

  • Design algorithms for automated IHC scoring of TMEM31 expression

  • Develop companion diagnostic approaches based on TMEM31 detection

  • Create patient selection tools for TMEM31-targeted clinical trials

These computational approaches complement experimental work and can accelerate the translation of basic TMEM31 research findings into clinically relevant applications, particularly in developing personalized cancer vaccines targeting this antigen .