Recombinant Pongo abelii Motile sperm domain-containing protein 1 (MOSPD1)

How does MOSPD1 differ from other members of the VAP protein family?

MOSPD1 belongs to the expanded VAP (VAMP-associated protein) family, which includes VAPA, VAPB, MOSPD1, MOSPD2, and MOSPD3. While all these proteins contain MSP domains, they have distinct motif binding preferences:

The key distinction is that MOSPD1 and MOSPD3 prefer to interact with FFNT (two phenylalanines in a neutral tract) motifs rather than the acidic FFAT (two phenylalanines in an acidic tract) motifs preferred by VAPA, VAPB, and MOSPD2 .

What is the role of MOSPD1 in mesenchymal stem cell differentiation?

Research has shown that MOSPD1 plays a critical role in the proliferation and differentiation of mesenchymal stem cells (MSCs). Studies using MOSPD1-null embryonic stem cells (ESCs) revealed:

• Significantly reduced formation of mesenchymal progenitor colonies in CFU-F assays

• Decreased expression of MSC markers (CD90, CD73, and CD105) in MOSPD1-null cells

• Impaired proliferation of MOSPD1-derived MSCs

• Reduced ability to differentiate into osteoblasts and adipocytes

• Decreased production of hematopoietic progenitors

Interestingly, the ability to differentiate into chondrocytes and cardiomyocytes was not significantly affected in MOSPD1-null cells, suggesting that MOSPD1 affects specific mesenchymal lineages rather than being required for all mesenchymal differentiation pathways .

How does MOSPD1 function in membrane contact sites (MCS)?

MOSPD1 serves as an ER-localized tether protein that facilitates the formation of membrane contact sites between the endoplasmic reticulum and other organelles. By interacting with proteins containing FFNT motifs, MOSPD1 can recruit these proteins to the ER membrane. This function is similar to but distinct from other VAP family proteins:

The varied abundance of these proteins suggests they may have specialized roles in different cell types or conditions .

What are the optimal methods for producing recombinant Pongo abelii MOSPD1?

Recombinant Pongo abelii MOSPD1 can be produced in several expression systems, each with advantages and limitations:

For most basic research applications, E. coli-expressed MOSPD1 (residues 1-158 or full-length) with a purification tag (His, GST) is sufficient. The protein should be stored in Tris-based buffer with 50% glycerol at -20°C for extended stability .

What experimental design considerations are critical for studying MOSPD1's role in mesenchymal differentiation?

When designing experiments to study MOSPD1's role in mesenchymal differentiation, researchers should consider:

• Replication: Multiple biological replicates (minimum n=3) are essential to account for variability in differentiation outcomes.

• Randomization: Samples should be randomized during processing to prevent systematic errors.

• Controls: Include positive controls (wild-type cells), negative controls (known differentiation inhibitors), and technical controls for each assay.

• Differentiation assays: Multiple measures of differentiation should be used:

  • Colony-forming unit-fibroblast (CFU-F) assays

  • Flow cytometry for MSC markers (CD90, CD73, CD105)

  • Functional differentiation assays (osteoblast, adipocyte, chondrocyte)

  • Proliferation assays (cell counting, MTT/XTT assays)

• Timecourse analysis: Differentiation should be assessed at multiple timepoints (e.g., days 0, 3, 7, 14, 21) to capture the dynamic process.

• Gene expression analysis: qRT-PCR to confirm MOSPD1 levels and assess lineage markers .

How can researchers effectively analyze contradictory data about MOSPD1's role in epithelial-mesenchymal transition (EMT)?

Previous research produced contradictory findings regarding MOSPD1's role in epithelial-mesenchymal transition (EMT). When analyzing contradictory data, researchers should:

• Examine methodological differences: Contradictions may arise from different experimental systems (cell lines, knockout vs. knockdown approaches, timing of analyses).

• Perform targeted validation experiments: Design experiments specifically to test the contradictory findings:

  • If siRNA knockdown of MOSPD1 showed altered EMT marker expression but MOSPD1-null cells did not, test both approaches in parallel.

  • Examine expression of key EMT markers (Snai1, Snai2, Cdh1, Cdh11) at multiple timepoints.

• Consider cell-type specificity: The study by Dixon et al. found that while MOSPD1 did not affect EMT marker expression in embryonic stem cells, it did impact mesenchymal stem cell differentiation, suggesting context-dependent functions.

• Use multiple readouts: Combine gene expression, protein levels, and functional assays to build a more complete picture.

Example data from contradictory studies:

The current consensus suggests MOSPD1 may not regulate EMT per se, but rather affects subsequent MSC proliferation and differentiation .

What approaches should be used to analyze MOSPD1's potential role in breast cancer progression?

Recent research has implicated MOSPD1 in breast cancer progression. To effectively analyze this relationship, researchers should:

• Conduct differential expression analysis: Compare MOSPD1 expression between breast cancer and normal tissues using multiple datasets (TCGA, GEO).

• Perform survival analysis: Correlate MOSPD1 expression levels with clinical outcomes using Kaplan-Meier analysis.

• Design functional studies: Include both gain-of-function (overexpression) and loss-of-function (knockdown/knockout) approaches in breast cancer cell lines.

• Analyze immune correlations: Assess the relationship between MOSPD1 expression and:

  • Stromal and immune cell infiltration (using ESTIMATE algorithm)

  • Expression of immune checkpoint genes

  • Response to immunotherapies like anti-PD-L1

• Develop multivariate models: Create and validate nomograms incorporating MOSPD1 expression with other clinical features to predict survival.

Recent findings from such approaches revealed that:

• MOSPD1 expression is significantly elevated in breast cancer samples compared to normal tissues

• High MOSPD1 expression correlates with poor clinical outcomes

• MOSPD1 suppression inhibits tumor growth

• MOSPD1 silencing enhances sensitivity to anti-PD-L1 therapy

• MOSPD1 affects Th2 cell activity in the tumor microenvironment .

How conserved is MOSPD1 across species, and what implications does this have for research model selection?

MOSPD1 shows significant conservation across mammalian species, suggesting fundamental biological importance. When selecting research models, consider:

The high conservation between human and Pongo abelii MOSPD1 (98% identity) makes recombinant Pongo abelii MOSPD1 an excellent surrogate for human protein in many applications. For in vivo studies of development and disease, mouse models remain the standard, though researchers should validate key findings across models when possible .

How do the biochemical properties of recombinant MOSPD1 compare when produced in different expression systems?

The choice of expression system significantly impacts the biochemical properties of recombinant MOSPD1:

For studies focusing on basic MSP domain interactions with FFNT motifs, E. coli-produced protein is sufficient. For studies of complex cellular functions or therapeutic applications, higher eukaryotic expression systems are preferred .

What are the most promising approaches for studying MOSPD1's potential as a therapeutic target in cancer?

Given MOSPD1's emerging role in breast cancer, several approaches show promise for exploring its therapeutic potential:

• Structure-based drug design: Using the MSP domain structure to design small molecule inhibitors that disrupt MOSPD1-FFNT interactions.

• Combination therapy assessment: Testing MOSPD1 inhibition alongside established therapies, particularly:

  • Checkpoint inhibitors (anti-PD-1/PD-L1) given the observed enhancement of sensitivity

  • Conventional chemotherapies to assess potential synergies

• Biomarker development: Validating MOSPD1 as a predictive biomarker for response to specific therapies or as a prognostic marker.

• Cell-specific targeting strategies: Developing antibody-drug conjugates or nanoparticles that specifically target cells with high MOSPD1 expression.

• Gene therapy approaches: Using CRISPR-Cas9 or siRNA delivery systems to selectively inhibit MOSPD1 in tumor cells.

Research suggests that combining MOSPD1 inhibition with immunotherapy may be particularly effective, as MOSPD1 silencing enhanced sensitivity to anti-PD-L1 therapy in preclinical models .

What unresolved questions remain about MOSPD1's function in membrane contact sites?

Despite recent advances, several key questions about MOSPD1's role in membrane contact sites remain unresolved:

• Subcellular distribution: How does MOSPD1 localize to specific regions of the ER, and does this differ from other VAP family proteins?

• Temporal dynamics: How are MOSPD1-mediated contacts regulated in response to cellular signals or stress?

• FFNT-containing partners: What is the complete repertoire of FFNT-containing proteins that interact with MOSPD1?

• Functional redundancy: To what extent can other VAP family proteins compensate for MOSPD1 deficiency?

• Lipid transfer function: Does MOSPD1 facilitate lipid transfer between organelles, as demonstrated for some other contact site proteins?

• Disease relevance: How do alterations in MOSPD1-mediated contacts contribute to specific disease states?

Recent studies have begun identifying MOSPD1-specific interacting partners using proximity labeling approaches (BioID), but a comprehensive understanding of the MOSPD1 interactome and its functional significance remains to be established .

What are the optimal conditions for studying MOSPD1-FFNT interactions in vitro?

For robust analysis of MOSPD1-FFNT interactions, researchers should consider these optimized conditions:

• Protein preparation:

  • Express the MSP domain of MOSPD1 (residues 1-158) with a purification tag

  • Ensure >90% purity by SDS-PAGE

  • Verify proper folding using circular dichroism

• Buffer conditions:

  • 20 mM Tris-HCl, pH 7.5

  • 150 mM NaCl

  • 1 mM DTT

  • 5% glycerol

• Interaction analysis methods:

  • Surface Plasmon Resonance (SPR): Immobilize MOSPD1 on CM5 chip, flow FFNT-containing peptides

  • Isothermal Titration Calorimetry (ITC): Direct measurement of binding affinity and thermodynamics

  • Microscale Thermophoresis (MST): Requires less protein, good for screening multiple peptides

  • Pull-down assays: Use GST-MOSPD1 with biotinylated FFNT peptides

• Controls:

  • Include FFAT peptides as negative controls

  • Use VAPA/B with FFAT and FFNT peptides for comparison

  • Test mutated FFNT sequences to validate specificity

Typical binding parameters for wild-type MOSPD1-FFNT interactions show KD values in the range of 0.6-1.0 μM, similar to the affinity of VAP proteins for FFAT motifs .

What experimental design strategies are most effective for studying MOSPD1's role in cancer progression?

To effectively study MOSPD1's role in cancer progression, researchers should implement these experimental design strategies:

• Cell line selection:

  • Use multiple breast cancer cell lines representing different subtypes (luminal, HER2+, triple-negative)

  • Include non-tumorigenic breast epithelial cell lines as controls

  • Consider patient-derived organoids for higher clinical relevance

• Genetic manipulation approaches:

  • CRISPR-Cas9 knockout for complete loss-of-function

  • Inducible shRNA for temporal control of knockdown

  • Overexpression using lentiviral systems for gain-of-function

• In vivo models:

  • Orthotopic xenograft models with MOSPD1-modified cells

  • Patient-derived xenografts with MOSPD1 inhibition

  • Syngeneic models to assess immune interactions

• Multidimensional analysis:

  • Tumor growth/proliferation (volume, Ki67 staining)

  • Metastatic potential (invasion assays, circulating tumor cells)

  • Immune infiltration (flow cytometry, immunohistochemistry)

  • Response to therapies (chemotherapy, immunotherapy, targeted agents)

• Statistical considerations:

  • Power analysis to determine appropriate sample sizes

  • Block randomization to control for confounding variables

  • Blinded assessment of outcomes to prevent bias

A comprehensive experimental approach combining these elements has revealed that MOSPD1 inhibition can reduce tumor growth and enhance sensitivity to immunotherapy, suggesting potential therapeutic applications .

How can researchers integrate various datasets to better understand MOSPD1's biological functions?

To comprehensively understand MOSPD1's functions, researchers should integrate multiple types of data:

• Multi-omics integration:

  • Transcriptomics: RNA-seq to identify co-expressed genes and affected pathways

  • Proteomics: Mass spectrometry to map protein-protein interactions

  • Metabolomics: Changes in lipid profiles when MOSPD1 is altered

  • Genomics: Mutations or copy number variations affecting MOSPD1

• Network analysis approaches:

  • Protein-protein interaction (PPI) networks using STRING or BioGRID

  • Gene co-expression networks to identify functional modules

  • Pathway enrichment analysis using GSEA or ClusterProfiler

• Integration methods:

  • Weighted correlation network analysis (WGCNA)

  • Multi-omics factor analysis (MOFA)

  • Joint non-negative matrix factorization

• Visualization tools:

  • Cytoscape for network visualization

  • R packages (ggplot2, ComplexHeatmap) for multi-dimensional data

A recent study employed GSEA to identify functional differences between high and low MOSPD1 expression states in breast cancer, revealing significant enrichment in immune-related pathways. The study also used the ESTIMATE algorithm to correlate MOSPD1 expression with stromal and immune scores, providing insights into its role in the tumor microenvironment .

What bioinformatic tools are most suitable for analyzing MOSPD1's role in protein-protein interaction networks?

For analyzing MOSPD1's role in protein-protein interaction networks, these specialized bioinformatic tools are most effective:

• Interaction prediction tools:

  • FFAT/FFNT motif prediction algorithms (Slee and Levine algorithm)

  • Structure-based interaction prediction (PRISM, ZDOCK)

  • Co-evolution-based prediction (GREMLIN, EVcouplings)

• Network analysis tools:

  • STRING (Search Tool for the Retrieval of Interacting Genes/Proteins)

  • Cytoscape with network analysis plugins

  • NetworkAnalyst for integrative analysis

  • MCODE or ClusterONE for identifying protein complexes

• Functional enrichment tools:

  • Gene Ontology enrichment analysis

  • Reactome pathway analysis

  • KEGG pathway mapping

  • DisGeNET for disease associations

• Visualization approaches:

  • Force-directed network layouts

  • Hierarchical clustering of interaction partners

  • Differential interaction heatmaps