Recombinant Mouse Motile sperm domain-containing protein 1 (Mospd1)

What is Mospd1 and what are its known functions?

Mospd1 (Motile Sperm Domain-Containing Protein 1) is a protein containing a motile sperm domain (MSP) that was originally identified in Caenorhabditis elegans sperm cells in the early 1980s. The protein appears to play critical roles in cellular differentiation and proliferation processes. According to expression databases like GTEx Portal, Mospd1 is expressed across various tissues including esophageal mucosa, adrenal gland, testis, skin, and uterus, suggesting diverse physiological functions .

In mice, Mospd1 is abundantly expressed in mesenchymal tissues, and its expression is elevated during differentiation in osteoblastic, myoblastic, and adipocytic cell lines . Functionally, Mospd1 appears to be involved in the differentiation and/or proliferation of mesenchymal stem cells. Multiple studies have indicated that Mospd1-null embryonic stem cells can proliferate but fail to differentiate into osteoblasts, adipocytes, and hematopoietic progenitors .

How is Mospd1 gene expression regulated?

Mospd1 expression is regulated by the Wnt/β-catenin signaling pathway. Specifically, research has identified that the β-catenin/TCF7L2 complex regulates MOSPD1 through an enhancer element located in the 3'-flanking region of the gene . ChIP-qPCR assays using anti-TCF7L2 antibody have demonstrated an enrichment of this 3'-enhancer region by 10.3-fold in precipitants compared to normal IgG controls .

What is the structure and molecular characteristics of recombinant mouse Mospd1?

Recombinant mouse Mospd1 is a protein with a molecular weight of approximately 24.1 kDa . Unlike some related proteins like VAPs (VAMP-associated proteins) which dimerize through a coiled-coil domain, the MSP domain of the related MOSPD2 protein appears to be monomeric as shown by size exclusion chromatography with multi-angle light scattering analysis . Though specific structural data for mouse Mospd1 is limited in the provided search results, this information can guide researchers in experimental design.

For experimental purposes, recombinant mouse Mospd1 can be expressed in various host systems including HEK-293 cells and cell-free protein synthesis systems. The recombinant protein is typically tagged with affinity markers such as His-tag or Strep-tag to facilitate purification and detection .

What role does Mospd1 play in epithelial-mesenchymal transition (EMT) and cancer progression?

Mospd1 appears to play a significant role in epithelial-mesenchymal transition (EMT), a critical process in cancer invasion and metastasis. Knockdown studies in mouse osteoblast cell line MC3T3-E1 have demonstrated that reducing Mospd1 expression induces the expression of epithelial cadherin Cdh1 while decreasing the expression of mesenchymal markers including Snai1, Snai2, and mesenchymal cadherin Cdh11 . These findings suggest Mospd1's involvement in promoting or maintaining the mesenchymal phenotype.

In cancer contexts, MOSPD1 has been found to be significantly upregulated in breast cancer (BC) tissues compared to normal tissues, with elevated expression correlating with poor clinical outcomes . Functional studies have demonstrated that MOSPD1 suppression inhibits tumor growth, while overexpression accelerates it . The correlation between MOSPD1 expression and N stage in breast cancer patients further supports its role in disease progression:

This data shows a statistically significant association between MOSPD1 expression and lymph node involvement, suggesting its potential role in cancer progression and metastasis .

How can Mospd1 be effectively silenced or overexpressed in experimental models?

For Mospd1 manipulation in experimental models, researchers have successfully employed several approaches:

For silencing Mospd1:

• RNA interference (RNAi) using short hairpin RNA (shRNA) targeting Mospd1 has been effectively used in both in vitro cell culture and in vivo mouse models. Transfection with shRNA control vector (sh-NC) serves as an appropriate control .

• CRISPR-Cas9 genome editing can be used to generate Mospd1 knockout cell lines for more permanent gene silencing.

• Antisense oligonucleotides targeting Mospd1 mRNA can provide transient knockdown.

For overexpressing Mospd1:

• Transfection with expression vectors containing the Mospd1 coding sequence (oe-MOSPD1) has been successfully implemented, with empty vectors serving as controls (oe-NC) .

• Viral delivery systems (lentiviral, adenoviral) can be used for more efficient transduction in difficult-to-transfect cell types.

When establishing tumor transplantation models to study Mospd1 function in vivo, mammary fat pad injections with manipulated cancer cell lines have proven effective. Experimental designs typically include four groups: oe-NC, oe-MOSPD1, sh-NC, and sh-MOSPD1, allowing for comprehensive analysis of both gain and loss of function .

What methodologies are used to assess Mospd1 binding interactions with other proteins?

To study Mospd1 protein-protein interactions, several techniques have been employed:

• String analysis tools can be used to predict potential protein-protein interaction relationships, with interactions having a composite score greater than 0.4 considered statistically significant .

• Surface plasmon resonance (SPR) can measure binding affinities between Mospd1 and potential binding partners. This technique has been used to study the related MOSPD2 protein's interactions with FFAT motif-containing proteins, revealing micromolar affinities (Kᴅ of 0.9 ± 0.2 μM) .

• Size exclusion chromatography combined with multi-angle light scattering analysis (SEC-MALS) can determine the quaternary structure of the protein, which is important for understanding binding stoichiometry .

• Co-immunoprecipitation (Co-IP) followed by mass spectrometry can identify novel binding partners.

• Chromatin immunoprecipitation followed by quantitative PCR (ChIP-qPCR) has been used to demonstrate binding of transcription factors like TCF7L2 to the enhancer regions of MOSPD1, helping to understand its transcriptional regulation .

How does Mospd1 expression correlate with immune function in cancer contexts?

Emerging research suggests important connections between MOSPD1 and immune function in cancer, particularly in breast cancer. Silencing MOSPD1 has been shown to enhance breast cancer cell sensitivity to anti-PD-L1 therapy and decrease T helper 2 (Th2) cell activity . This suggests that MOSPD1 may modulate tumor immune microenvironment.

Using the ESTIMATE algorithm to evaluate stromal and immune scores in breast cancer samples, researchers have explored correlations between MOSPD1 expression and immune infiltration. Additionally, co-expression analysis has revealed relationships between MOSPD1 and immune checkpoint genes .

To investigate these relationships, researchers typically employ:

• Computational approaches using R packages such as "limma," "reshape2," and "RColorBrewer"

• Flow cytometry to analyze immune cell populations in tumor samples

• Multiplex immunohistochemistry to visualize immune cell distribution

• In vivo models combining MOSPD1 manipulation with immune checkpoint inhibitors

The experimental design for these studies often includes multiple groups to test interactions, such as: sh-NC, sh-MOSPD1, sh-NC + anti-PD-L1, sh-MOSPD1 + anti-PD-L1, oe-NC, oe-MOSPD1, oe-NC + anti-PD-L1, and oe-MOSPD1 + anti-PD-L1 .

What statistical methods are appropriate for analyzing Mospd1 expression data in cancer studies?

Several statistical approaches have been validated for analyzing Mospd1 expression data:

• For comparing Mospd1 expression between tumor and normal tissues, the unpaired t-test with Benjamini-Hochberg correction for multiple testing is appropriate . This approach was used in analyzing gene expression values from the Gene Expression Omnibus (GSE21510) dataset.

• To determine correlation between MOSPD1 expression and known Wnt target genes, Pearson's correlation coefficient (r) provides a measure of linear association .

• For ChIP-qPCR data analysis comparing enrichment of specific DNA regions, the unpaired t-test is suitable .

• When analyzing reporter assay data with multiple experimental conditions, one-way analysis of variance (ANOVA) followed by Tukey's or Dunnett's multiple comparisons test should be employed .

• For identifying differentially expressed genes (DEGs) between high and low MOSPD1 expression groups, the DESeq2 package in R with Wilcoxon rank-sum test can be used, applying thresholds of |log2FC|>1 and adjusted P-value<0.05 .

Data should be presented as mean ± standard deviation (SD), with P<0.05 considered statistically significant. Software packages like BellCurve for Excel or R can be used for these analyses .

What are the optimal conditions for expression and purification of recombinant mouse Mospd1?

Based on available data, the following conditions appear optimal for recombinant mouse Mospd1 production:

• Expression Systems:

  • HEK-293 cells provide a mammalian expression system yielding protein with proper post-translational modifications .

  • Cell-free protein synthesis (CFPS) systems offer an alternative approach that can achieve 70-80% purity as determined by SDS-PAGE .

• Purification Tags:

  • His-tag or Strep-tag fusion constructs facilitate purification using affinity chromatography .

  • Protein purity can be assessed by SDS-PAGE, Western Blot, and analytical SEC (HPLC) .

• Storage Conditions:

  • Store the purified protein at -80°C to maintain stability .

  • Avoid repeated freeze-thaw cycles which can degrade protein quality .

• Quality Control:

  • Purity should be >90% as determined by Bis-Tris PAGE for HEK-293 expressed protein, or >70-80% for CFPS systems .

  • Confirm identity and activity using anti-tag ELISA, Western Blot, and functional assays .

How can researchers effectively design experiments to investigate Mospd1's role in the Wnt/β-catenin signaling pathway?

To investigate Mospd1's relationship with the Wnt/β-catenin pathway, researchers should consider these methodological approaches:

• Enhancer Element Analysis:

  • Chromatin Immunoprecipitation followed by qPCR (ChIP-qPCR) using anti-TCF7L2 antibody and region-specific primer sets to confirm interaction between transcription factors and the 3'-enhancer region of MOSPD1 .

  • Include positive controls such as known enhancer regions (e.g., intron 2 of RNF43) .

• Transcriptional Regulation Studies:

  • Reporter assays using luciferase constructs containing wild-type and mutated versions of the MOSPD1 enhancer element to assess transcriptional activity .

  • Assess effects of Wnt pathway activators (e.g., Wnt3a, GSK3β inhibitors) and inhibitors on MOSPD1 expression.

• Correlation Analysis:

  • Analyze correlation between MOSPD1 expression and known Wnt target genes in cancer datasets using Pearson's correlation coefficient .

  • Explore public databases such as Gene Expression Omnibus for relevant datasets.

• Functional Studies:

  • Knockdown or overexpress components of the Wnt/β-catenin pathway and assess effects on MOSPD1 expression.

  • Conversely, manipulate MOSPD1 levels and examine effects on Wnt target gene expression.

Statistical analysis should include experimental replication in biological triplicates, with data presented as mean ± standard deviation .

What is the potential of Mospd1 as a therapeutic target in cancer treatment?

The potential of Mospd1 as a therapeutic target in cancer is supported by several lines of evidence:

• Differential Expression: MOSPD1 is significantly upregulated in breast cancer tissues compared to normal tissues, suggesting cancer-specific targeting potential .

• Clinical Correlations: High MOSPD1 expression correlates with poor clinical outcomes in breast cancer patients, indicating its prognostic relevance .

• Functional Effects: Experimental suppression of MOSPD1 inhibits tumor growth, while overexpression accelerates it, demonstrating a direct role in cancer progression .

• Immune Checkpoint Interactions: Silencing MOSPD1 enhances cancer cell sensitivity to anti-PD-L1 therapy, suggesting potential for combination therapeutic approaches .

To develop Mospd1-targeted therapies, researchers might pursue:

• Small molecule inhibitors targeting Mospd1 protein-protein interactions

• Antisense oligonucleotides or siRNAs to downregulate Mospd1 expression

• Antibody-drug conjugates targeting cancer cells with high Mospd1 expression

• Combination approaches with immune checkpoint inhibitors like anti-PD-L1

Methodologically, nomogram construction using multivariate analysis results can help predict survival probabilities (1, 3, and 5-year) and evaluate the efficacy of Mospd1-targeted interventions .

How might Mospd1 function differ between developmental processes and pathological contexts?

Mospd1 appears to have context-dependent functions that differ between normal development and disease states:

In developmental contexts:

• Mospd1 is abundantly expressed in mesenchymal tissues during development .

• It plays critical roles in differentiation of osteoblasts, adipocytes, and hematopoietic progenitors .

• Mospd1-null embryonic stem cells maintain proliferative capacity but lose differentiation potential .

• Expression of Runx2 and Osteocalcin, key factors in osteoblastic differentiation, is regulated by Mospd1 .

In pathological contexts:

• MOSPD1 appears to promote epithelial-mesenchymal transition in cancer cells, contributing to invasion and metastasis .

• It modulates immune responses in the tumor microenvironment, affecting sensitivity to immunotherapy .

• Ovarian cancer cells with high invasion phenotypes show significantly increased MOSPD1 expression compared to low-invasion phenotypes .

To investigate these contextual differences, researchers should employ:

• Tissue-specific and inducible knockout models

• Temporal expression analysis during development versus disease progression

• Single-cell transcriptomics to identify cell-type specific functions

• Comparative pathway analysis between developmental and pathological contexts

What are the most sensitive detection methods for quantifying Mospd1 expression in tissue samples?

For optimal detection and quantification of Mospd1 in research settings, several complementary methods can be employed:

• Transcriptional Level Analysis:

  • Quantitative real-time PCR (qRT-PCR) provides sensitive detection of Mospd1 mRNA levels

  • RNA-Seq offers comprehensive transcriptome analysis with potential to detect splice variants

  • Droplet digital PCR (ddPCR) enables absolute quantification with high precision

• Protein Level Detection:

  • Western blotting using specific anti-Mospd1 antibodies with appropriate controls

  • Enzyme-linked immunosorbent assay (ELISA) for quantitative protein detection

  • Immunohistochemistry (IHC) for spatial localization within tissue samples

• High-Throughput Analysis:

  • Tissue microarrays combined with IHC for screening multiple samples

  • Protein microarrays for proteomic profiling

  • Mass spectrometry for detailed protein characterization

For validation purposes, recombinant Mospd1 proteins can serve as positive controls . When analyzing clinical samples, calibration curves should be established using purified recombinant protein standards. Statistical analysis of expression data should incorporate appropriate normalization to housekeeping genes (for mRNA) or loading controls (for protein).

How can researchers integrate Mospd1 expression data with other omics datasets?

To comprehensively understand Mospd1 function, researchers should integrate multiple data types:

• Transcriptomics Integration:

  • Compare gene expression profiles between high and low MOSPD1 expression groups to identify differentially expressed genes (DEGs) using the DESeq2 package in R .

  • Apply statistical thresholds (|log2FC|>1 and adjusted P-value<0.05) to determine significance .

  • Identify co-expression networks using Pearson's correlation analysis .

• Proteomics Integration:

  • Utilize String analysis tools to predict protein-protein interaction networks, considering interactions with composite scores >0.4 as significant .

  • Validate key interactions using co-immunoprecipitation followed by mass spectrometry.

• Immunomics Integration:

  • Calculate stromal score, immune score, and estimation score using the ESTIMATE algorithm .

  • Analyze correlations between MOSPD1 expression and immune parameters using R packages like "limma" and "estimate" .

  • Investigate relationships between MOSPD1 and immune checkpoint genes through co-expression analysis .

• Clinical Data Integration:

  • Construct nomograms based on multivariate analysis to predict survival probabilities .

  • Evaluate model performance through calibration curves and concordance index (c-index) calculation .

  • Employ ROC analysis to compare predictive accuracy of nomograms with individual prognostic factors .

This integrated approach allows researchers to place Mospd1 within broader biological networks and understand its system-wide effects.

What bioinformatic tools are most appropriate for analyzing Mospd1 in large genomic datasets?

For comprehensive analysis of Mospd1 in large genomic datasets, researchers should employ:

• Differential Expression Analysis:

  • DESeq2 (version 3.8 or later) package in R for identifying differentially expressed genes between high and low Mospd1 expression groups .

  • Wilcoxon rank-sum test for statistical testing, with |log2FC|>1 and adjusted P-value<0.05 as thresholds .

• Network Analysis:

  • String analysis tool for predicting protein-protein interactions (PPI) .

  • "limma," "reshape2," and "RColorBrewer" R packages for co-expression analysis between Mospd1 and immune checkpoint genes .

• Microenvironment Analysis:

  • ESTIMATE algorithm to calculate stromal score, immune score, and estimation score in tumor samples .

• Survival Analysis:

  • "RMS" R package to generate nomograms for predicting survival probabilities .

  • Bootstrap method with 1000 resamples to compute the concordance index (c-index) .

  • ROC analysis to assess prediction accuracy .

• Public Database Integration:

  • Gene Expression Omnibus (GEO) for accessing datasets like GSE21510 .

  • GTEx Portal for examining normal tissue expression patterns .

When applying these tools, researchers should ensure proper normalization of data, correction for multiple testing (e.g., Benjamini-Hochberg method), and validation of findings using independent datasets .