Recombinant Macaca fascicularis Methylsterol monooxygenase 1 (MSMO1)

What is Methylsterol monooxygenase 1 (MSMO1) and what is its primary function?

Methylsterol monooxygenase 1 (MSMO1), also known as C-4 methylsterol oxidase or by the synonym sterol-C4-methyl oxidase analog (SC4MOL), is an enzyme that plays a critical role in cholesterol biosynthesis. It catalyzes the demethylation at the C-4 position during sterol synthesis with the enzyme classification number EC 1.14.13.72 . MSMO1 displays essential functions in the normal synthesis of cholesterol, which is a fundamental component of cell membranes and a precursor for steroid hormones .

The protein is encoded by the MSMO1 gene (also known by the synonym SC4MOL). In its native form, MSMO1 is involved in metabolic pathways related to lipid metabolism and sterol biosynthesis, particularly in the conversion of lanosterol to cholesterol. This enzymatic activity is crucial for maintaining cellular lipid homeostasis .

What is the molecular structure of Macaca fascicularis MSMO1?

The Macaca fascicularis (crab-eating macaque or cynomolgus monkey) MSMO1 protein consists of 293 amino acids in its full-length form. Its amino acid sequence is:

MATNESVSIFSSSLAVEYV DSLLPENPLQEPFKNAWNY LNNYTKFQIATWGSLIVHEA LYFSFCLPGFLFQFIPYMK KYKIQKDKPETWENQWKCF KVLLFNHFCIQLPLICGTY YFTEYFNIPYDWERMPRWY FLLARCFGCAVIEDTWHYF MHRLLHHKRIYKYIHKVHH EFQAPFGMEAEYAHPLETL ILGTGFFIGIVLLCDHVIL LWAWVTIRLLETIDVHSGY DIPLNPLNLIPFYAGSRHH DFHHMNFIGNYASTFTWWD RIFGTDSQYHAYYEKKKKF EKKTE

This protein is classified in the UniProt database with the accession number Q4R4Q4. The recombinant form of this protein is typically produced for research applications with a tag (the specific tag type is usually determined during the production process) and is optimized with appropriate storage buffers to maintain protein stability and function .

How does MSMO1 expression vary between normal and cancerous tissues?

Research has demonstrated significant differences in MSMO1 expression between normal and cancerous tissues, though the direction of dysregulation appears to be cancer-type specific.

In cervical cancer, MSMO1 expression is significantly increased compared to non-tumor cervical tissues (P<0.001). This up-regulation correlates with disease progression, as patients with stage III-IV disease show higher MSMO1 expression levels than those with stage I-II disease (P=0.04). Additionally, patients who did not achieve complete response to primary treatments had higher MSMO1 levels than those who had a complete response (P<0.001) .

Conversely, in pancreatic cancer, down-regulation of MSMO1 has been reported to promote cancer development and progression. This suggests that MSMO1 may have tumor-suppressive functions in pancreatic tissue .

These contrasting findings highlight the complex, tissue-specific roles of MSMO1 in carcinogenesis and underscore the importance of context-specific analysis when studying this protein in different cancer types.

What are the optimal storage and handling conditions for recombinant MSMO1?

For researchers working with recombinant Macaca fascicularis MSMO1, the following storage and handling protocols are recommended to maintain protein integrity and activity:

The recombinant protein is typically provided in a Tris-based buffer with 50% glycerol, optimized specifically for MSMO1 stability. For short-term storage (up to one week), working aliquots can be maintained at 4°C. For longer-term storage, the protein should be kept at -20°C, while extended storage periods require conservation at -20°C or -80°C .

It is crucial to note that repeated freezing and thawing cycles significantly degrade protein quality and should be avoided. Best practices include:

• Upon receipt, dividing the stock solution into small working aliquots

• Minimizing freeze-thaw cycles by thawing only the required amount

• Thawing samples on ice and returning unused portions to appropriate storage temperatures promptly

• Maintaining sterile conditions when handling the protein to prevent contamination

These precautions help preserve the structural integrity and biological activity of the recombinant protein for experimental applications .

How can researchers effectively analyze MSMO1 expression patterns in clinical samples?

Based on methodologies described in recent research, several approaches have been validated for analyzing MSMO1 expression patterns in clinical samples:

RNA-based expression analysis:
Real-time Quantitative PCR (qRT-PCR) has been successfully employed to measure MSMO1 mRNA expression. This technique requires:

• RNA extraction using TRIZOL reagent

• Standardizing RNA levels across samples via nucleotide testing

• cDNA synthesis using appropriate reagents (e.g., TaKaRa RNAiso Reagent)

• Amplification under specific conditions (95°C for 30s, followed by 45 cycles of 95°C for 5s and 60°C for 45s)

• Data analysis using the -ΔΔCt method with melt-curve dissociation to evaluate amplification quality

Large-scale database analysis:
For broader expression pattern analysis across multiple samples or cancer types, researchers can leverage publicly available databases:

• TCGA database for comprehensive cancer genomics data

• KM-plotter database for survival analysis correlation

• Gene Expression Profiling Interactive Analysis (GEPIA)

• GEO database for additional expression datasets

Statistical methods for analyzing expression differences include:

• Wilcoxon rank sum test for comparing expression between tumor and non-tumor samples

• Kruskal-Wallis test for analyzing expression across multiple subgroups

• Logistic regression for correlating expression with clinical features

• Cox regression for survival analysis

For accurate results, it is recommended to include adequate biological replicates (at least three independent experiments) and appropriate controls in all expression analyses.

What in vivo models are appropriate for studying MSMO1 function in cancer progression?

Cell-line-derived tumor xenograft models have been successfully employed to investigate the function of MSMO1 in cancer progression. Based on published methodologies, the following protocol has proven effective:

Subcutaneous tumor model in mice:

• Select appropriate mice strain (e.g., C57B6) at approximately 4 weeks of age

• Transfect cancer cell lines (e.g., PANC-02 for pancreatic cancer studies) with MSMO1 siRNA or negative control

• Prepare cell suspension in PBS at a concentration of 1×10^6 cells/ml

• Inject 100 μl of cell suspension subcutaneously, preferably in the middle of the armpit

• Use a cotton swab to reduce bleeding and prevent overflow of cell suspension

• Monitor tumor growth for an appropriate period (e.g., four weeks)

• Measure tumor dimensions using a vernier caliper and calculate volume using the formula: V=1/2×a×b^2 (where a is the long axis, b is the short axis)

• Harvest tumors for subsequent analysis:

  • Portion for protein extraction

  • Portion for histological analysis (H&E staining)

  • Additional analyses as required

This model allows for direct assessment of how MSMO1 modulation affects tumor growth and progression in vivo.

For more complex studies investigating metastasis or tumor microenvironment interactions, orthotopic models may be more appropriate, though these require more sophisticated surgical techniques for implantation of tumor cells into the organ of origin.

How does MSMO1 expression correlate with clinical outcomes in cancer patients?

Multiple studies have investigated the relationship between MSMO1 expression and clinical outcomes in cancer patients, with significant findings emerging from recent research:

In cervical squamous cell carcinoma (CESC), MSMO1 expression has been found to have strong prognostic value. High MSMO1 expression is associated with:

• Advanced disease stage - Patients with stage III-IV disease show significantly higher MSMO1 expression than those with stage I-II disease (P=0.04)

• Poor response to therapy - Patients who failed to achieve complete response to primary treatments had higher MSMO1 levels than those with complete response (P<0.001)

• Correlation with age - MSMO1 expression was significantly correlated with patient age (Chi-squared test, P=0.01; Wilcoxon test, P=0.003)

The detailed correlation between MSMO1 expression and clinical characteristics in cervical cancer patients is presented in the following table:

These findings suggest that MSMO1 can serve as a prognostic marker in cervical cancer patients, with high expression indicating a higher likelihood of poor outcomes .

What molecular mechanisms underlie the role of MSMO1 in cancer progression?

The molecular mechanisms by which MSMO1 contributes to cancer progression appear to be context-dependent and involve several key cellular processes:

In cervical cancer (up-regulation scenario):
While complete mechanistic details require further investigation, high MSMO1 expression has been linked to:

• Enhanced cell proliferation

• Resistance to primary therapies

• Possible interactions with immune cell infiltration, particularly T cells

In pancreatic cancer (down-regulation scenario):
Down-regulation of MSMO1 has been associated with:

• Promotion of Epithelial-mesenchymal transition (EMT) - a critical process in cancer metastasis

• Enhanced cell proliferation

• Potential involvement in the PI3K/AKT/mTOR signaling pathway - MSMO1 may regulate downstream targets (p-AKT, p-PI3K, and p-mTOR) in modulating the EMT process

These divergent findings in different cancer types highlight the complex, tissue-specific roles of MSMO1 in carcinogenesis. The underlying mechanisms may involve MSMO1's fundamental role in cholesterol biosynthesis, which impacts membrane fluidity, lipid raft formation, and various signaling pathways that regulate cell proliferation, survival, and migration.

Future mechanistic studies should focus on identifying the direct interaction partners of MSMO1 and the downstream signaling cascades that mediate its effects on cancer progression in different tissue contexts.

How can MSMO1 be targeted for potential therapeutic applications?

Based on current research, several approaches could be explored for targeting MSMO1 in therapeutic applications:

For cancers with MSMO1 up-regulation (e.g., cervical cancer):

• RNA interference (RNAi): siRNA or shRNA targeting MSMO1 could reduce its expression in cancer cells

• CRISPR/Cas9 gene editing: To knock out or reduce MSMO1 expression in tumor cells

• Small molecule inhibitors: Development of specific inhibitors targeting MSMO1's enzymatic activity

• Combination therapies: Using MSMO1 inhibitors in combination with standard chemotherapeutic agents to enhance treatment efficacy

For cancers with MSMO1 down-regulation (e.g., pancreatic cancer):

• Gene therapy approaches: To restore MSMO1 expression in cancer cells

• Targeting downstream effectors: Identifying and targeting the molecular pathways activated by MSMO1 down-regulation

• Metabolic modulation: Interventions targeting cholesterol metabolism pathways to compensate for MSMO1 dysregulation

Diagnostic applications:
MSMO1 may serve as a valuable biomarker for patient stratification and treatment selection. In cervical cancer, for example, detecting MSMO1 expression may help improve diagnostic accuracy and predict treatment response and prognosis .

Before clinical implementation, extensive validation studies are needed to:

• Confirm the efficacy and specificity of MSMO1-targeting approaches

• Evaluate potential off-target effects

• Assess the impact on normal cellular functions that depend on cholesterol biosynthesis

• Determine optimal dosing and administration strategies for any MSMO1-directed therapies

What are the limitations of current methods for studying MSMO1 function?

Current methodologies for studying MSMO1 function face several important limitations that researchers should consider:

Expression analysis limitations:

• Tissue heterogeneity: Cancer samples often contain mixed populations of cells (tumor cells, stromal cells, immune cells), which can confound expression analysis results

• Post-translational modifications: mRNA expression studies (e.g., qRT-PCR) don't capture post-translational modifications that may affect MSMO1 function

• Splice variants: Potential alternative splicing of MSMO1 may not be detected by standard PCR primers targeting specific regions

Functional analysis challenges:

• Compensatory mechanisms: Knockdown or overexpression of MSMO1 may trigger compensatory pathways that mask or complicate interpretation of results

• Model system limitations: Cell line models may not fully recapitulate the complexity of in vivo tumor microenvironments

• Species differences: While studies in Macaca fascicularis MSMO1 provide valuable insights, functional differences may exist between species, requiring validation in human systems

Technical considerations:

• Antibody specificity: Commercial antibodies for MSMO1 may have cross-reactivity issues or limited specificity

• Protein stability: MSMO1 protein stability during experimental procedures may affect functional assay results

• Assay sensitivity: Detection of subtle changes in enzyme activity requires highly sensitive and optimized assays

To overcome these limitations, researchers should consider:

• Using multiple complementary approaches to study MSMO1 function

• Validating findings across different model systems

• Employing single-cell analysis techniques to address tissue heterogeneity

• Developing more specific reagents for MSMO1 detection and functional analysis

How can researchers resolve contradictory findings regarding MSMO1's role in different cancer types?

The contrasting roles of MSMO1 in different cancer types (up-regulated in cervical cancer but down-regulated in pancreatic cancer) present a significant challenge for researchers. To resolve these seemingly contradictory findings, several methodological approaches can be employed:

Comparative molecular profiling:

• Conduct parallel analyses of MSMO1 expression, mutations, and epigenetic modifications across multiple cancer types

• Identify tissue-specific co-factors or interacting partners that may modulate MSMO1 function

• Examine the broader cholesterol biosynthesis pathway for tissue-specific alterations that might explain contextual differences

Integration of multi-omics data:

• Combine transcriptomics, proteomics, and metabolomics analyses to build a comprehensive picture of MSMO1's role

• Correlate MSMO1 expression with global pathway alterations to identify cancer-specific dependencies

• Employ bioinformatic approaches to predict differential protein-protein interactions in different tissue contexts

Functional validation approaches:

• Perform reciprocal experiments - silence MSMO1 in cervical cancer models and overexpress it in pancreatic cancer models

• Develop isogenic cell lines with controlled MSMO1 expression to isolate its effects from other genetic variables

• Use domain-specific mutants to identify which protein regions are critical for its context-dependent functions

Mechanistic studies:

• Track changes in cholesterol metabolism following MSMO1 modulation in different cancer types

• Investigate transcription factors and epigenetic regulators that control MSMO1 expression in a tissue-specific manner

• Examine microenvironmental factors that might influence MSMO1's role in different anatomical contexts

By employing these systematic approaches, researchers can begin to unravel the complex, context-dependent functions of MSMO1 and develop a unified model that explains its apparently contradictory roles in different cancer types.

What quality control measures are essential when working with recombinant MSMO1?

When working with recombinant Macaca fascicularis Methylsterol monooxygenase 1, implementing rigorous quality control measures is essential to ensure experimental reliability and reproducibility:

Protein purity and integrity assessment:

• SDS-PAGE analysis: To confirm the expected molecular weight (~33 kDa) and assess purity

• Western blotting: Using specific anti-MSMO1 antibodies to verify protein identity

• Mass spectrometry: For precise molecular weight determination and sequence verification

• Size exclusion chromatography: To detect protein aggregation or degradation

Functional verification:

• Enzyme activity assays: Measuring the conversion of substrate to product to confirm enzymatic activity

• Binding assays: Verifying interaction with known binding partners

• Circular dichroism: To assess proper protein folding and secondary structure

Storage condition validation:

• Stability testing: Analyzing protein integrity after storage at different temperatures (-80°C, -20°C, 4°C)

• Freeze-thaw impact assessment: Evaluating activity after multiple freeze-thaw cycles

• Buffer optimization: Testing various buffers and additives for optimal stability

Contaminant testing:

• Endotoxin testing: Using Limulus Amebocyte Lysate (LAL) assay to detect bacterial endotoxins

• Microbial contamination: Sterility testing to ensure absence of bacterial or fungal contamination

• Host cell protein analysis: Confirming minimal presence of proteins from the expression system

Lot-to-lot consistency:

• Maintaining detailed documentation of production parameters

• Establishing acceptance criteria for batch release

• Comparing critical quality attributes between different lots

Implementing these quality control measures will help ensure that experimental results using recombinant MSMO1 are reliable, reproducible, and accurately reflect the protein's true biological properties rather than artifacts of poor protein quality or contamination.

What emerging technologies could advance our understanding of MSMO1 biology?

Several cutting-edge technologies hold promise for deepening our understanding of MSMO1 biology and its role in health and disease:

Single-cell omics approaches:

• Single-cell RNA sequencing to map MSMO1 expression at cellular resolution in heterogeneous tissues

• Single-cell proteomics to detect cell-specific variations in MSMO1 protein levels and modifications

• Spatial transcriptomics to correlate MSMO1 expression with tissue architecture and microenvironmental features

Advanced imaging techniques:

• Super-resolution microscopy to visualize MSMO1 subcellular localization with nanometer precision

• Live-cell imaging with fluorescently tagged MSMO1 to monitor dynamic changes in localization and interactions

• Correlative light and electron microscopy to link MSMO1 function to ultrastructural features

CRISPR-based technologies:

• CRISPR activation/interference (CRISPRa/CRISPRi) for precise, titratable control of MSMO1 expression

• CRISPR base editing for introducing specific point mutations to study structure-function relationships

• CRISPR screens to identify synthetic lethal interactions and compensatory pathways

Protein structure and interaction analyses:

• Cryo-electron microscopy to determine high-resolution MSMO1 structure

• Hydrogen-deuterium exchange mass spectrometry to map conformational dynamics

• Proximity labeling approaches (BioID, APEX) to identify context-specific MSMO1 interactors in living cells

Metabolomic approaches:

• Targeted metabolomics to profile cholesterol intermediates and related metabolites following MSMO1 modulation

• Flux analysis using isotope-labeled precursors to track metabolic changes in sterol biosynthesis pathways

• Lipidomics to characterize global lipid alterations resulting from MSMO1 dysregulation

These technologies, especially when applied in combination, have the potential to resolve current contradictions in MSMO1 research and provide a comprehensive understanding of its complex, context-dependent functions in normal physiology and disease.

How might MSMO1 research contribute to precision medicine approaches in cancer treatment?

MSMO1 research shows considerable promise for advancing precision medicine approaches in cancer treatment through several key avenues:

Patient stratification and prognostic markers:
The differential expression of MSMO1 across cancer types and its correlation with clinical outcomes suggests its potential as a biomarker for patient stratification. In cervical cancer, for example, MSMO1 expression has shown strong diagnostic power, with high expression associated with poor prognosis . This could enable more personalized treatment planning based on individual tumor characteristics.

Targeted therapy development:
Understanding the molecular mechanisms by which MSMO1 contributes to cancer progression could lead to the development of novel targeted therapies. For cancers where MSMO1 is upregulated (like cervical cancer), specific inhibitors could be developed. Conversely, for cancers where MSMO1 is downregulated (like pancreatic cancer), strategies to restore expression or target downstream effectors could be pursued.

Combination therapy optimization:
MSMO1's involvement in cholesterol metabolism suggests potential synergies with existing therapies. For example, combining MSMO1-targeting agents with statins or other metabolic modulators might enhance effectiveness. Research could identify the most promising combination approaches for specific cancer types and molecular profiles.

Biomarker-driven clinical trials:
MSMO1 expression could serve as a selection criterion for clinical trials, allowing for more homogeneous patient populations and potentially clearer efficacy signals. This approach could accelerate the development of new therapies by focusing on patients most likely to benefit.

Resistance mechanism identification:
Understanding how MSMO1 contributes to treatment response could help identify mechanisms of resistance. For instance, the observation that patients who did not achieve complete response to primary cervical cancer treatments had higher MSMO1 levels suggests its potential role in treatment resistance .

Liquid biopsy development:
If MSMO1 or its metabolic products can be detected in circulation, this could enable non-invasive monitoring of treatment response and disease progression. Research into circulating markers related to MSMO1 activity could lead to valuable clinical tools.

As research progresses, integrating MSMO1 status into comprehensive molecular profiling of tumors could contribute to more personalized treatment algorithms that consider not just genetic mutations but also metabolic adaptations in cancer.

What interdisciplinary approaches could enhance MSMO1 research in the context of various diseases?

Advancing MSMO1 research will benefit significantly from interdisciplinary approaches that bridge multiple scientific fields:

Systems biology integration:

• Computational modeling of sterol metabolism networks to predict the systemic effects of MSMO1 modulation

• Multi-scale modeling connecting molecular-level MSMO1 function to cellular and tissue-level outcomes

• Network analysis to position MSMO1 within broader metabolic and signaling pathways across different tissues

Immunology and microbiome connections:

• Investigating how MSMO1-dependent cholesterol metabolism affects immune cell function and cancer immunosurveillance

• Exploring potential interactions between microbiome-derived metabolites and MSMO1 activity

• Examining how MSMO1-related lipid changes influence inflammation and immune response in the tumor microenvironment

Chemical biology approaches:

• Development of small molecule probes to track MSMO1 activity in living systems

• Chemical genetics screens to identify context-specific modifiers of MSMO1 function

• Fragment-based drug discovery to develop highly specific MSMO1 modulators

Developmental biology perspectives:

• Tracking MSMO1 expression and function across development to understand tissue-specific roles

• Investigating potential links between developmental programs and MSMO1's divergent roles in different cancer types

• Using developmental model systems to identify evolutionarily conserved aspects of MSMO1 function

Translational medicine integration:

• Biobanking initiatives to collect and characterize patient samples with well-annotated MSMO1 status

• Patient-derived xenograft and organoid models to validate MSMO1-targeting approaches in personalized contexts

• Early-phase clinical studies incorporating MSMO1 biomarkers and pharmacodynamic endpoints