MITD1 Antibody

What is MITD1 and what cellular functions does it perform?

MITD1 is a protein containing an N-terminal microtubule-interacting and trafficking (MIT) domain and a C-terminal phospholipase D-like (PLD) domain that binds membranes . It functions primarily in:

• Abscission during cytokinesis, coordinating with ESCRT-III proteins to facilitate the final separation of daughter cells

• Antiviral activity against flaviviruses including West Nile virus, Usutu virus, Zika virus, and dengue virus

• Potential tumor suppression in certain cancers by inhibiting cell proliferation and migration

The protein exists as a homodimer and localizes to the midbody during the terminal stages of cell division, particularly when the midbody appears very thin .

What are the standard applications for MITD1 antibodies in research?

MITD1 antibodies have been validated for multiple experimental applications:

Most commercially available MITD1 antibodies show reactivity with human, mouse, and rat samples, detecting the protein at approximately 29-30 kDa .

How is MITD1 expression regulated in different tissues and cell types?

• In the brain, MITD1 expression is specifically induced in microglial cells (the primary immune cells of the central nervous system)

• MITD1 expression is not increased by type I interferon (IFN-I) in most human cells and mouse tissues examined, suggesting tissue-specific regulation mechanisms

• At the single-cell level, MITD1 shows highest expression in T cells (87.7%) compared to other cell types

• MITD1 expression varies significantly across cancer types, with upregulation in some cancers and downregulation in others compared to corresponding normal tissues

What controls should be included when using MITD1 antibodies for Western blot experiments?

When using MITD1 antibodies for Western blot experiments, researchers should incorporate:

• Positive controls: Use cell lines with confirmed MITD1 expression, such as MCF-7, HeLa, or HEK-293 cells

• Negative controls: Consider MITD1 knockdown samples (siRNA-treated cells) to confirm antibody specificity

• Loading controls: Standard housekeeping proteins such as GAPDH, β-actin, or α-tubulin

• Molecular weight marker: To confirm the expected band size of approximately 29-30 kDa

• Antibody validation: Test the antibody on multiple cell lines to ensure consistent detection (e.g., MCF-7, Saos-2, U2OS, LNCaP, HeLa, HEK-293, HSC-T6, NIH/3T3, 4T1 cells)

Always optimize antibody dilution (typically 1:500-1:10000 for Western blot) based on your specific experimental conditions .

How should researchers optimize MITD1 antibody use for immunohistochemistry studies?

For optimal IHC results with MITD1 antibodies:

• Antigen retrieval: Perform microwave antigen retrieval with 10 mM PBS buffer pH 7.2 before commencing with the IHC staining protocol

• Antibody dilution: Start with a dilution of 1:100 and optimize as needed

• Tissue selection: For baseline studies, consider liver tissue which has been successfully used for MITD1 IHC

• Controls: Include both positive control tissues with known MITD1 expression and negative controls (omitting primary antibody)

• Detection method: Use appropriate secondary antibodies and visualization systems compatible with your primary antibody's host species (typically rabbit or mouse)

• Counterstaining: Apply appropriate nuclear counterstain for context and cellular localization

• Quantification: Consider digital image analysis for objective quantification of staining intensity and distribution

How can researchers effectively study MITD1's role in viral infections using antibody-based approaches?

To investigate MITD1's antiviral properties:

• Expression analysis: Use Western blot with MITD1 antibodies to measure MITD1 expression levels before and after viral infection or interferon treatment

• Localization studies: Employ immunofluorescence with MITD1 antibodies to visualize subcellular localization during viral infection, particularly focusing on potential colocalization with viral replication factories

• Functional studies: Combine MITD1 antibody detection with:

  • MITD1 overexpression or knockdown experiments

  • Viral load quantification (plaque assays, qPCR, etc.)

  • Assessment of viral replication factory formation

• Interaction analysis: Use co-immunoprecipitation with MITD1 antibodies to identify interactions with ESCRT-III proteins and viral components

• Tissue-specific expression: Apply MITD1 antibodies in IHC to examine expression in brain tissues, particularly in microglial cells during neurotropic flavivirus infections

Research indicates MITD1 inhibits flavivirus replication by sequestering specific ESCRT-III proteins involved in viral replication factory formation .

What methodological approaches are recommended for investigating MITD1's function in cytokinesis?

To study MITD1's role in cell division:

• Temporal expression analysis: Use time-course Western blot or immunofluorescence with MITD1 antibodies to track expression throughout the cell cycle

• Live-cell imaging: Combine MITD1 antibody staining with live-cell microscopy to observe MITD1 localization during cytokinesis

• Colocalization studies: Perform dual immunofluorescence with MITD1 antibodies and markers for:

  • Midbody components (CEP55, CHMP1B, IST1)

  • Microtubules (β-tubulin)

  • ESCRT-III proteins that interact with MITD1

• Loss-of-function analysis: Use siRNA knockdown of MITD1 followed by:

  • Quantification of multinucleated cells

  • Assessment of intercellular bridges

  • Time-lapse imaging of cell division

  • Analysis of membrane stability

• Structure-function analysis: Express wild-type and mutant MITD1 (MIT domain mutations or PLD domain mutations) and assess their localization and function using domain-specific antibodies

Research shows MITD1 depletion results in cytokinesis defects, indicated by increased multinucleated cells and membrane instabilities .

How should researchers interpret contradictory data regarding MITD1's role in different cancer types?

When facing contradictory findings about MITD1 in cancer research:

• Cancer-specific expression analysis: Use MITD1 antibodies with tissue microarrays to systematically compare expression across multiple cancer types and corresponding normal tissues

• Correlation with clinical parameters: Analyze MITD1 expression in relation to:

  • Cancer stage and grade

  • Patient survival

  • Treatment response

  • Molecular subtypes

• Functional studies: Perform MITD1 overexpression and knockdown experiments in multiple cancer cell lines, followed by:

  • Proliferation assays (CCK-8, EdU incorporation)

  • Migration and invasion assays (wound healing, Transwell)

  • Clonogenic assays

• Context-dependent analysis: Consider the cellular context, including:

  • Tumor microenvironment

  • Immune infiltration

  • Expression in specific cell populations (tumor cells vs. stromal cells vs. immune cells)

• Molecular pathway analysis: Use MITD1 antibodies in combination with other markers to investigate associations with:

  • DNA damage repair pathways

  • Immune checkpoint molecules

  • Tumor mutational burden and microsatellite instability

Research indicates MITD1 has dual roles: upregulated in renal cell carcinoma and associated with poor prognosis in some cancers (ACC, GBMLGG, LIHC, KIRC), but inhibits proliferation and migration in breast cancer and correlates with better prognosis in other cancers (BLCA, BRCA, OV, READ) .

How can researchers resolve specificity issues when working with MITD1 antibodies?

To address antibody specificity concerns:

• Validation in multiple systems:

  • Test the antibody across several cell lines with known MITD1 expression

  • Compare results between different application methods (WB, IHC, IF)

  • Use positive control recombinant MITD1 protein

• Knockdown/knockout validation:

  • Use siRNA or CRISPR to generate MITD1-depleted samples

  • Confirm loss of signal in these samples

  • Include rescue experiments with siRNA-resistant MITD1

• Cross-reactivity assessment:

  • Test for potential cross-reactivity with related proteins containing MIT domains

  • Use bioinformatic analysis to identify potential cross-reactive epitopes

  • Consider blocking peptide experiments if cross-reactivity is suspected

• Antibody comparison:

  • Compare results using antibodies from different manufacturers

  • Use antibodies targeting different epitopes of MITD1

  • Consider monoclonal vs. polyclonal antibodies based on experimental needs

• Isotype controls:

  • Use appropriate isotype control antibodies (e.g., IgG1 for mouse monoclonal antibodies)

  • Match concentration of isotype control to primary antibody

What are the key considerations when studying MITD1 in the context of immune cell populations?

When investigating MITD1 in immune contexts:

• Cell type-specific analysis:

  • Use flow cytometry with MITD1 antibodies to analyze expression across immune cell subsets

  • Apply single-cell techniques to identify specific populations with high MITD1 expression (T cells show highest expression at 87.7%)

  • Consider cell sorting followed by Western blot for quantitative comparisons

• Activation state assessment:

  • Analyze MITD1 expression before and after immune cell activation

  • Correlate with activation markers and cytokine production

  • Assess the impact of interferon stimulation on MITD1 levels

• Tissue-specific immune populations:

  • Compare MITD1 expression in tissue-resident immune cells (particularly microglia in the brain)

  • Use IHC with co-staining for immune cell markers and MITD1

  • Consider laser capture microdissection for isolated population analysis

• Tumor microenvironment analysis:

  • Examine correlations between MITD1 expression and immune infiltration

  • Analyze associations with immune, stromal, and ESTIMATE scores

  • Investigate relationships with specific immune cell types (CD4+ T cells, B cells, CD8+ T cells, neutrophils, dendritic cells, and macrophages)

• Functional consequences:

  • Assess how MITD1 expression affects immune cell function and communication

  • Investigate impact on cytokine production and cell-cell signaling

  • Consider potential roles in immune response to viral infection or cancer

How might researchers investigate MITD1 as a potential biomarker for immunotherapy response?

To explore MITD1's potential as an immunotherapy biomarker:

• Retrospective analysis:

  • Use MITD1 antibodies for IHC on tissue samples from patients who received immunotherapy

  • Correlate MITD1 expression with treatment response and survival outcomes

  • Compare predictive power with established biomarkers (PD-1, PD-L1, CTLA-4, IFN-γ)

• Mechanism exploration:

  • Investigate relationships between MITD1 and:

    • Tumor mutational burden (TMB)

    • Microsatellite instability (MSI)

    • Homologous recombination deficiency (HRD)

    • Ploidy

  • Analyze associations with immune checkpoint molecule expression

• Multiparameter analysis:

  • Combine MITD1 expression data with other clinical and molecular parameters

  • Develop and validate predictive models or nomograms

  • Assess model performance across different cancer types

• Functional validation:

  • Examine how MITD1 modulation affects response to immune checkpoint inhibitors in preclinical models

  • Investigate potential mechanisms (cGAS-STING pathway activation, immune cell recruitment)

  • Study impact on the tumor microenvironment

• Prospective clinical studies:

  • Design prospective studies measuring MITD1 expression before immunotherapy

  • Establish standardized protocols for MITD1 detection and quantification

  • Define thresholds for "high" versus "low" expression in clinical contexts

Research indicates MITD1 expression is positively correlated with TMB and MSI in several cancers, suggesting potential predictive value for immunotherapy response .

What methodologies are recommended for investigating the interaction between MITD1 and ESCRT-III proteins?

To study MITD1-ESCRT-III interactions:

• Co-immunoprecipitation approaches:

  • Use MITD1 antibodies to pull down MITD1 and associated proteins

  • Detect specific ESCRT-III components (CHMP1B, IST1) in immunoprecipitates

  • Perform reciprocal co-IP with antibodies against ESCRT-III proteins

  • Include appropriate controls (IgG control, MITD1-depleted samples)

• Domain-specific interaction analysis:

  • Generate constructs expressing:

    • Full-length MITD1

    • MIT domain alone

    • MITD1 lacking MIT domain (ΔMIT)

  • Assess interaction with ESCRT-III proteins

  • Identify specific MIT-MIM1 (MIT interacting motif) interactions

• Structural approaches:

  • Use purified proteins for crystallography studies

  • Perform site-directed mutagenesis of key interaction residues

  • Validate mutant effects using binding assays and functional studies

• Localization studies:

  • Perform co-localization analysis using dual immunofluorescence

  • Focus on midbody localization during cytokinesis

  • Analyze recruitment dynamics using live-cell imaging

  • Compare wild-type and mutant protein localization patterns

• Functional consequence assessment:

  • Examine how disrupting MITD1-ESCRT-III interactions affects:

    • Cytokinesis

    • Viral replication factory formation

    • Membrane remodeling events

  • Compare phenotypes of MITD1 depletion versus ESCRT-III depletion

Research demonstrates that MITD1 interacts with a subset of ESCRT-III proteins through its MIT domain, and this interaction mediates MITD1 recruitment to the midbody during cytokinesis .

How should researchers integrate MITD1 expression data across multiple cancer datasets?

For comprehensive pan-cancer MITD1 analysis:

• Standardized expression analysis:

  • Use consistent protocols for MITD1 antibody-based detection

  • Normalize expression data across different platforms and studies

  • Apply batch correction methods when integrating multiple datasets

  • Consider both mRNA and protein expression levels

• Multi-omics integration:

  • Correlate MITD1 protein expression (detected by antibodies) with:

    • mRNA expression data

    • Genomic alterations affecting MITD1

    • Epigenetic modifications

    • Proteomic profiles

  • Use bioinformatic tools designed for multi-omics data integration

• Clinical annotation correlation:

  • Maintain consistent clinical parameter definitions across datasets

  • Apply standardized statistical methods for survival analysis

  • Use multivariate models to account for confounding factors

  • Consider cancer subtype-specific analyses

• Validation across cohorts:

  • Test findings from discovery datasets in independent validation cohorts

  • Assess consistency of results across different patient populations

  • Implement cross-validation strategies for predictive models

  • Address potential selection biases in retrospective datasets

• Visualization and reporting:

  • Use consistent visualization methods for comparing MITD1 expression

  • Report detailed methodological approaches for data integration

  • Provide access to analysis code and procedures

  • Address limitations and heterogeneity in integrated datasets

Research demonstrates the value of this approach, revealing that MITD1 expression varies significantly across cancer types, with different prognostic implications depending on the specific cancer context .

What are the best practices for analyzing contradictory results in MITD1 functional studies?

When facing contradictory results:

• Methodological comparison:

  • Evaluate differences in experimental approaches:

    • Antibody sources, clones, and validation methods

    • Cell lines and culture conditions

    • Knockdown/overexpression efficiency

    • Assay sensitivity and specificity

  • Replicate experiments using standardized protocols

• Context-dependent analysis:

  • Consider cell type-specific effects:

    • Compare results across different cell lines

    • Evaluate primary cells versus established cell lines

    • Assess normal versus cancer cells

  • Examine microenvironmental influences

• Temporal considerations:

  • Analyze time-dependent effects:

    • Acute versus chronic MITD1 modulation

    • Cell cycle phase-specific functions

    • Time course of cellular responses

• Pathway interaction assessment:

  • Map MITD1 function within signaling networks:

    • Identify potential compensatory mechanisms

    • Assess pathway cross-talk

    • Evaluate feedback regulation

  • Consider combinatorial effects with other proteins

• Systematic review approach:

  • Apply formal systematic review methodology:

    • Define clear inclusion/exclusion criteria

    • Extract data using standardized forms

    • Assess quality of evidence

    • Perform meta-analysis where appropriate

  • Address publication bias and selective reporting