Recombinant Mouse Transmembrane protein 71 (Tmem71)

What is Tmem71 and what are its primary biological functions?

Tmem71 (Transmembrane protein 71) is a protein-coding gene that appears to function as a potential oncogene, particularly in brain tumors. Research indicates that Tmem71 is involved in multiple crucial biological processes including:

• Immune and inflammatory responses

• Cell proliferation and migration

• Chemotaxis

• Response to drugs and therapeutic agents

Tmem71 has been significantly linked to glioma pathogenesis, with expression patterns that correlate with tumor aggressiveness. Additionally, it has been identified as a component of the mRBPome (mRNA-binding proteome) in certain cell types, suggesting potential roles in post-transcriptional regulation .

How is Tmem71 expression distributed across different grades of glioma?

Tmem71 expression demonstrates a distinct pattern across glioma grades, with expression levels increasing proportionally with tumor grade. Multiple dataset analyses reveal:

• Significantly higher expression in glioblastoma multiforme (GBM, grade IV) compared to lower-grade gliomas

• Progressive increase in expression correlating with increasing histological grades

• Particularly elevated expression in the mesenchymal molecular subtype of glioma

The correlation between Tmem71 expression and tumor grade makes it a potential biomarker for disease progression and aggressiveness in glioma patients.

What molecular characteristics of gliomas are associated with elevated Tmem71 expression?

Tmem71 expression shows significant associations with specific molecular characteristics:

• Higher expression in IDH-wild-type gliomas compared to IDH-mutant tumors

• Elevated expression in MGMT-unmethylated samples

• Significantly upregulated in the mesenchymal molecular subtype (AUC values of 86.4% and 86.6% in CGGA and TCGA datasets, respectively)

• Association with stemness markers in glioma stem cells (GSCs)

• Correlation with TMZ-resistant phenotypes

These associations suggest Tmem71 may play a role in defining more aggressive molecular subtypes of glioma and potentially contribute to therapy resistance mechanisms.

What are the recommended methods for recombinant mouse Tmem71 production?

For effective production of recombinant mouse Tmem71 protein:

• Expression System Selection: Mammalian expression systems (particularly HEK293 or CHO cells) are recommended for transmembrane proteins to ensure proper folding and post-translational modifications.

• Vector Design:

  • Include a signal peptide for proper membrane insertion

  • Incorporate a purification tag (His-tag or FLAG-tag) at either N- or C-terminus depending on predicted topology

  • Consider using inducible expression systems to control protein production

• Purification Strategy:

  • For membrane-bound proteins, detergent-based extraction (e.g., n-dodecyl β-D-maltoside or CHAPS) is often required

  • Affinity chromatography using the incorporated tag

  • Size exclusion chromatography for further purification

• Validation Methods:

  • Western blotting to confirm molecular weight and expression

  • Mass spectrometry for sequence verification

  • Functional assays based on known activities

How should researchers design experiments to investigate Tmem71's role in chemoresistance?

To effectively study Tmem71's contribution to chemoresistance:

• Cell Model Selection:

  • Use paired chemosensitive and chemoresistant glioma cell lines (such as those analyzed in the TMZ resistance studies)

  • Include patient-derived xenograft models when possible

  • Consider using the M059J (radiosensitive) and M059K (radioresistant) cell line models

• Expression Modulation Experiments:

  • Knockdown using validated shRNA or CRISPR-Cas9 approaches in resistant cells

  • Overexpression in sensitive cells

  • Dose-response analysis with TMZ or other chemotherapeutics following expression modulation

• Mechanistic Investigations:

  • RNA-seq to identify downstream gene expression changes

  • Co-immunoprecipitation to identify protein interaction partners

  • Analysis of drug transport, metabolism, and DNA repair mechanisms

• In vivo Validation:

  • Orthotopic xenograft models with controlled Tmem71 expression

  • Treatment with standard chemotherapy regimens

  • Analysis of tumor growth, survival, and molecular response markers

What approaches are most effective for studying Tmem71 in patient-derived glioma samples?

For optimal analysis of Tmem71 in clinical specimens:

• Tissue Handling and Processing:

  • Rapid fixation in formalin for FFPE samples or flash-freezing for fresh tissue

  • RNA extraction protocols optimized for FFPE tissue using specialized kits (e.g., MagMAX FFPE DNA/RNA Ultra kit)

  • Reverse transcription using high-quality systems (e.g., NEBNext RNA First Strand Synthesis Module)

• Expression Analysis Methods:

  • RNA-seq for comprehensive transcriptomic profiling

  • RT-qPCR for targeted expression analysis

  • Immunohistochemistry with validated antibodies

  • Single-cell RNA-seq for heterogeneity assessment

• Clinical Correlation Analysis:

  • Integration with patient demographic and treatment data

  • Survival analysis using Kaplan-Meier and Cox regression models

  • Multivariate analysis controlling for known prognostic factors (IDH status, MGMT methylation)

  • ROC curve analysis to assess biomarker potential

How should researchers interpret Tmem71 expression data in relation to patient outcomes?

For meaningful interpretation of Tmem71 expression data:

• Statistical Approaches:

  • Kaplan-Meier survival analysis with log-rank tests for outcome differences

  • Cox proportional hazards models for multivariate analysis

  • Appropriate stratification based on clinical and molecular parameters

• Expression Threshold Determination:

  • Use ROC curve analysis to identify optimal cutoffs for high vs. low expression

  • Consider quartile-based divisions to examine dose-response relationships

  • Validate thresholds across independent datasets

• Integration with Clinical Variables:

  • Analysis based on established Cox regression model including:

• Consideration of Confounding Factors:

  • Treatment differences between patient cohorts

  • Molecular subtype distribution

  • Batch effects in multi-institutional data

What bioinformatic approaches are recommended for analyzing Tmem71-associated pathways?

For comprehensive pathway analysis:

• Dataset Selection and Preparation:

  • Use multiple datasets for cross-validation (e.g., TCGA, CGGA, GEO databases)

  • Implement proper normalization methods for RNA-seq or microarray data

  • Filter low-quality or low-expression genes

• Correlation Analysis:

  • Employ Pearson correlation analysis to identify genes co-expressed with Tmem71

  • Filter for significant correlations (r > 0.4 or r < -0.4, P < 0.05)

  • Validate correlations across independent datasets

• Functional Enrichment Analysis:

  • Use Gene Ontology (GO) analysis through platforms like DAVID

  • Apply GSEA (Gene Set Enrichment Analysis) for pathway identification

  • Consider specialized immune cell infiltration analyses given Tmem71's association with immune processes

• Network Analysis:

  • Construct protein-protein interaction networks

  • Apply modularity analysis to identify functional clusters

  • Integrate multi-omic data when available

What is known about the relationship between Tmem71 and immune checkpoint molecules in glioma?

Research has revealed significant associations between Tmem71 and immune regulatory pathways:

• Immune Checkpoint Correlations:

  • Strong positive correlations between Tmem71 and key immune checkpoint molecules including PD-1, PD-L1, TIM-3, and B7-H3

  • Functional enrichment of Tmem71-associated genes in immune and inflammatory response pathways

• Potential Mechanisms:

  • Tmem71 may influence the tumor immune microenvironment through direct or indirect regulation of immune checkpoint expression

  • Association with the mesenchymal subtype, which typically shows higher immune cell infiltration

  • Possible role in mediating immune evasion strategies in aggressive gliomas

• Therapeutic Implications:

  • Potential for combination approaches targeting both Tmem71 and immune checkpoint pathways

  • Consideration of Tmem71 expression when selecting patients for immunotherapy

  • Monitoring Tmem71 levels as a biomarker for immunotherapy response

How does Tmem71 contribute to glioma stem cell maintenance and therapeutic resistance?

Tmem71's role in glioma stem cell biology includes:

• Expression Pattern:

  • Significantly elevated expression in glioma stem cells compared to non-stem glioma cells

  • Association with stemness markers in various glioma datasets

• Functional Contributions:

  • Potential involvement in self-renewal pathways

  • Role in cell proliferation as evidenced by decreased expansion rates following Tmem71 knockdown (similar to effects seen with other RNA-binding proteins)

  • Significant increase in apoptosis following Tmem71 knockdown, suggesting a role in cell survival

• Resistance Mechanisms:

  • Overexpression in TMZ-resistant cells

  • Correlation with drug response pathways in GO analysis

  • Potential contribution to maintenance of stem-like state that promotes therapy resistance

What experimental approaches are most effective for studying Tmem71's RNA-binding functions?

To investigate Tmem71's potential RNA-binding properties:

• RNA-Protein Interaction Detection:

  • UV crosslinking and immunoprecipitation (CLIP) followed by sequencing (CLIP-seq)

  • RNA immunoprecipitation (RIP) assays

  • In vitro RNA binding assays with purified protein

  • mRBPome capture using oligo(dT)-conjugated beads after UV-crosslinking

• Target Validation Methods:

  • Reporter assays with wild-type and mutated binding sites

  • RNA stability assays following Tmem71 modulation

  • Polysome profiling to assess effects on translation

  • Direct binding assays with synthetic RNA oligos

• Structural Considerations:

  • Domain analysis to identify RNA-binding motifs

  • Mutagenesis of predicted RNA-binding domains

  • Co-crystal structure determination when feasible

• Functional Consequence Analysis:

  • Transcriptome-wide analysis after Tmem71 depletion

  • Assessment of alternative splicing patterns

  • Analysis of downstream protein expression changes

What approaches show promise for targeting Tmem71 as a therapeutic strategy in glioma?

Several strategies for therapeutic targeting of Tmem71 show potential:

• RNA Interference Approaches:

  • siRNA or shRNA delivery systems optimized for CNS penetration

  • Nanoparticle-based delivery of RNA interference molecules

  • Assessment of anti-tumor effects in preclinical models

• Small Molecule Development:

  • Screening for compounds that inhibit Tmem71 function or expression

  • Structure-based drug design if protein structure is available

  • Repurposing of existing drugs that may modulate Tmem71 activity

• Antibody-Based Approaches:

  • Development of antibodies against extracellular domains

  • Antibody-drug conjugates for targeted delivery

  • Combination with immune checkpoint inhibitors given the correlation with immune pathways

• Combination Strategies:

  • Tmem71 inhibition plus standard chemotherapy (TMZ)

  • Targeting of Tmem71 alongside radiotherapy

  • Multi-target approaches addressing both Tmem71 and related pathways

How can Tmem71 expression data be integrated into prognostic models for glioma patients?

For clinical implementation of Tmem71 as a prognostic marker:

• Model Development:

  • Integration with established prognostic factors (IDH status, MGMT methylation, age, KPS)

  • Development of multivariate models using Cox regression analysis

  • Machine learning approaches for complex pattern identification

• Validation Requirements:

  • Independent validation cohorts from multiple institutions

  • Prospective validation in clinical trials

  • Testing in diverse patient populations

• Implementation Considerations:

  • Standardization of Tmem71 measurement methods

  • Development of clinically-applicable assays (IHC or RT-PCR based)

  • Integration with molecular testing workflows

• Risk Stratification Application:

  • Development of risk scores incorporating Tmem71

  • Treatment decision guidance based on expression levels

  • Monitoring of expression changes during treatment

What are the main challenges in detecting Tmem71 protein in mouse brain tissue samples?

Researchers face several technical hurdles when studying Tmem71 protein:

• Antibody Specificity Issues:

  • Limited commercial antibody options with validated specificity

  • Cross-reactivity with related transmembrane proteins

  • Solution: Validate antibodies using knockout/knockdown controls and multiple detection methods

• Protein Extraction Challenges:

  • Difficulty extracting integral membrane proteins from brain tissue

  • Requirement for specialized detergent-based protocols

  • Solution: Optimize membrane protein extraction using gentle detergents and avoid excessive heating

• Low Expression Levels:

  • Potentially low abundance in normal brain tissue

  • Signal-to-noise ratio challenges

  • Solution: Consider signal amplification methods and highly sensitive detection systems

• Tissue Heterogeneity:

  • Cellular heterogeneity of brain tissue complicating expression analysis

  • Solution: Single-cell approaches, laser capture microdissection, or careful tissue microdissection

How can researchers optimize Tmem71 knockdown experiments in glioma models?

For effective Tmem71 functional studies:

• Target Sequence Selection:

  • Design multiple shRNA/siRNA sequences targeting different exons

  • Avoid sequences with off-target potential

  • Consider algorithms optimized for knockdown efficiency

• Delivery Method Optimization:

  • Lentiviral systems for stable knockdown

  • Inducible knockdown systems to study temporal effects

  • Electroporation for hard-to-transfect glioma stem cells

• Validation Approaches:

  • Quantify knockdown at both mRNA (RT-qPCR) and protein (Western blot) levels

  • Include appropriate non-targeting controls

  • Consider rescue experiments to confirm specificity of observed phenotypes

• Phenotypic Analysis Framework:

  • Comprehensive assessment of:

    • Proliferation (using methods like MTT, BrdU incorporation)

    • Apoptosis (Annexin V/PI staining)

    • Migration and invasion (transwell assays)

    • Stem cell properties (sphere formation assays)

    • Drug sensitivity (dose-response curves)

What are the most promising areas for future Tmem71 research in glioma biology?

Several key areas warrant further investigation:

• Mechanistic Studies:

  • Elucidation of molecular mechanisms by which Tmem71 promotes chemoresistance

  • Characterization of protein interaction networks and signaling pathways

  • Investigation of potential transcription factors regulating Tmem71 expression

• Functional Genomics:

  • CRISPR screens to identify synthetic lethal interactions with Tmem71

  • Identification of genes that modulate sensitivity to Tmem71 targeting

  • Exploration of genetic dependencies in Tmem71-high vs. Tmem71-low tumors

• Translational Applications:

  • Development of small molecule inhibitors or targeting strategies

  • Testing of combination approaches with standard-of-care treatments

  • Integration into precision medicine frameworks for glioma

• Broader Tissue Context:

  • Investigation of Tmem71 functions in other cancer types

  • Exploration of normal physiological roles in development and tissue homeostasis

  • Comparative studies across species to understand evolutionary conservation

How might single-cell technologies advance our understanding of Tmem71 function in heterogeneous tumor populations?

Single-cell approaches offer unique insights into Tmem71 biology:

• Cellular Heterogeneity Mapping:

  • Single-cell RNA-seq to identify Tmem71-expressing cell populations within tumors

  • Spatial transcriptomics to understand regional distribution

  • Correlation with stem cell markers at single-cell resolution

• Functional Heterogeneity:

  • Response to therapy at single-cell level

  • Clonal evolution patterns in relation to Tmem71 expression

  • Identification of resistance-associated cell states

• Microenvironmental Interactions:

  • Cell-cell communication analysis between Tmem71-high cells and immune populations

  • Interactions with vascular and stromal components

  • Influence on local tumor microenvironment

• Technological Approaches:

  • Single-cell ATAC-seq to examine chromatin accessibility

  • Cellular indexing of transcriptomes and epitopes (CITE-seq) for simultaneous protein and RNA analysis

  • Lineage tracing to understand cellular dynamics and differentiation hierarchies