mthfsd Antibody

What is MTHFSD protein and what are its known functions?

MTHFSD is a protein coding gene that produces methenyltetrahydrofolate synthetase domain-containing protein. It exists in four distinct isoforms:

• Isoform 1: 383 amino acids (42 kDa)

• Isoform 2: 382 amino acids (42 kDa)

• Isoform 3: 383 amino acids (42 kDa)

• Isoform 4: 382 amino acids (41 kDa)

Gene Ontology annotations indicate MTHFSD functions primarily include:

• Nucleic acid binding

• Nucleotide binding

• RNA binding activity

MTHFSD has been identified as a novel component of stress granules and interacts with several key proteins including TP53, TP63, TP73, SIN3A, and ACTR1A . Research indicates it may play an important role in RNA metabolism, particularly in neurodegenerative conditions like ALS .

What validation methods should be used to confirm MTHFSD antibody specificity?

When validating MTHFSD antibodies, researchers should employ multiple approaches:

• Western blotting validation:

  • Use MDA-MB453 cell line lysates (35 μg/lane) as a positive control, which has been shown to express detectable levels of MTHFSD protein

  • Confirm the presence of a band at the expected molecular weight (approximately 42 kDa)

  • Include appropriate controls (positive, negative, secondary antibody only)

• Immunohistochemistry validation:

  • Compare staining patterns with established MTHFSD localization (primarily nuclear in normal tissues)

  • Test multiple dilutions (1:200-1:500 recommended for IHC)

  • Validate across different tissue types with known expression levels

• Peptide competition assay:

  • Pre-incubate the antibody with blocking peptide (e.g., NBP1-82271PEP)

  • Compare staining patterns with and without peptide blocking

• KO/KD validation:

  • Use MTHFSD knockout or knockdown samples as negative controls when available

What are the recommended applications and dilutions for MTHFSD antibodies?

Based on validated protocols, MTHFSD antibodies can be used in several experimental techniques:

For optimal results:

• Determine antibody specificity using your specific experimental conditions

• Follow manufacturer's recommendations for storage (-20°C, avoid repeated freeze/thaw cycles)

• Use appropriate blocking solutions (typically 2.5% normal horse serum for IHC)

• For immunofluorescence, use DAKO antibody diluent and incubate overnight at 4°C

How does MTHFSD expression and localization change in neurodegenerative diseases?

Studies of MTHFSD in neurodegenerative conditions, particularly ALS, have revealed significant changes in expression and localization:

• Nuclear vs. Cytoplasmic Distribution:

  • In normal motor neurons: MTHFSD is predominantly localized in nuclei

  • In ALS: Reduced nuclear labeling and appearance of cytoplasmic granules

• Expression Changes in TDP-43 Mouse Models:

  • At 10 months (symptomatic stage): Significant downregulation of MTHFSD (>2-fold change) in motor neurons

  • At 5 months (presymptomatic): No significant changes observed

• Validation in Human ALS Samples:

  • Confirmed reduced nuclear MTHFSD labeling in human ALS cases

  • Similar pattern observed across both sporadic and familial ALS

These findings suggest MTHFSD mislocalization may contribute to ALS pathogenesis, potentially through disrupted RNA metabolism.

What methodological considerations are important for studying MTHFSD in tissue sections?

When examining MTHFSD in tissue sections, several methodological factors are critical:

• Tissue Preparation:

  • Use fresh tissue fixed in 10% formalin

  • For paraffin embedding, use 5 μm sections (3 mm for serial sections)

• Antigen Retrieval:

  • Use TE9 buffer (10 mM Trizma base, 1 mM EDTA, 0.05% Tween 20, pH 9.0)

  • Heat to 110°C in a pressure cooker for 15 minutes

• Recommended Antibodies:

  • For immunofluorescence: Rabbit anti-MTHFSD (AV40987, Sigma; 1:250 for mouse tissue, 1:50 for human tissue)

  • Alternative: Goat anti-MTHFSD (sc-82235, Santa Cruz; 1:50)

• Co-labeling Considerations:

  • Use SMI-32 (neurofilament marker) to identify motor neurons

  • Use G3BP as a stress granule marker when studying MTHFSD in stress granules

• Blocking and Signal Enhancement:

  • Block with 2.5% normal horse serum

  • For fluorescence microscopy, use appropriate Alexa Fluor secondary antibodies (1:500)

How can I optimize Western blot protocols for detecting MTHFSD?

For optimal Western blot detection of MTHFSD:

• Sample Preparation:

  • Load 35 μg of protein per lane

  • Use MDA-MB453 cell lysates as positive control

• Electrophoresis Conditions:

  • Use 10% SDS-PAGE gels

  • Transfer to PVDF membranes

• Blocking and Antibody Incubation:

  • Block with 5% skimmed milk powder in TBS for 1 hour at room temperature

  • Primary antibody: Dilute 1:1000 in blocking solution

  • Incubate overnight at 4°C

• Detection Method:

  • Use HRP-conjugated secondary antibodies (1:5000)

  • Visualize using ECL detection reagents

• Expected Results:

  • Primary band at approximately 42 kDa (calculated MW: 42.2 kDa)

  • May detect multiple isoforms depending on the antibody epitope

How can MTHFSD's role in stress granule formation be investigated?

MTHFSD has been identified as a novel component of stress granules. To investigate this function:

• Stress Granule Induction Models:

  • Arsenite treatment (commonly used stress inducer)

  • Heat shock

  • TDP-43 overexpression models

• Co-localization Studies:

  • Use dual immunofluorescence with MTHFSD antibody (1:250) and established stress granule markers:

    • G3BP (mouse anti-G3BP, 1:200, BD Transduction Laboratories)

    • TIA-1

    • eIF3

• RNA-Protein Interaction Analysis:

  • Perform RNA immunoprecipitation (RIP) to identify RNA targets

  • Use TRAP (translating ribosome affinity purification) to study translational changes:

    • Purify RNA using Absolutely RNA NanoPrep columns with on-column DNase digestion

    • Measure RNA integrity (RIN) for quality control

• Functional Analysis:

  • Create MTHFSD knockdown/knockout models to assess effects on stress granule formation

  • Examine changes in RNA metabolism and protein translation during stress

What is known about MTHFSD's relationship with TDP-43 and how can this be studied?

Research has identified a relationship between MTHFSD and TDP-43, particularly relevant to ALS pathogenesis:

• Evidence of Interaction:

  • TDP-43 binds to MTHFSD mRNA

  • Translational profile changes in TDP-43A315T mouse models show downregulation of MTHFSD

• RNA Immunoprecipitation Approach:

  • Use anti-TDP-43 antibodies to immunoprecipitate RNA-protein complexes

  • Analyze bound RNAs by RT-PCR or RNA-seq

  • Include appropriate controls (IgG, input samples)

  • Purify RNA from complexes using Absolutely RNA NanoPrep Kit with on-column DNase treatment

• Translating Ribosome Affinity Purification (TRAP) Methodology:

  • Generate mouse models expressing EGFP-L10a fusion protein in cholinergic neurons

  • Immunopurify EGFP-L10a using anti-EGFP monoclonal antibodies

  • Isolate actively translating mRNAs

  • Analyze using microarray or RNA-seq approaches

• Verification in ALS Models and Human Samples:

  • Compare expression and localization patterns between:

    • Control tissues

    • TDP-43 mouse models at different disease stages

    • Human ALS samples (sporadic and familial)

What advanced techniques can be used to study MTHFSD's molecular interactions?

To elucidate MTHFSD's molecular interactions and functional roles:

• Protein-Protein Interaction Analysis:

  • Co-immunoprecipitation with known interactors (TP53, TP63, TP73, SIN3A, ACTR1A)

  • Proximity ligation assay for in situ detection of protein interactions

  • Mass spectrometry analysis of immunoprecipitated complexes

• High-Resolution Imaging Approaches:

  • Super-resolution microscopy to examine subcellular localization

  • Live-cell imaging with fluorescently tagged MTHFSD to track dynamics

  • FRAP (Fluorescence Recovery After Photobleaching) to study mobility

• Functional Genomics:

  • CRISPR-Cas9 mediated knockout/knockin models

  • Domain-specific mutations to identify functional regions

  • Rescue experiments in knockdown/knockout models

• Systems Biology Integration:

  • Network analysis of MTHFSD interactome

  • Pathway enrichment analysis

  • Integration of proteomics, transcriptomics, and functional data

How can active learning approaches improve MTHFSD antibody characterization?

Advanced computational approaches can enhance antibody characterization:

• Machine Learning for Epitope Prediction:

  • Library-on-library approaches to identify specific antigen-antibody interactions

  • Out-of-distribution prediction models for novel epitopes

  • Reduction of required experimental data by up to 35% through active learning algorithms

• Active Learning Implementation:

  • Start with small labeled dataset of known MTHFSD antibody-antigen interactions

  • Iteratively expand labeled dataset based on model predictions

  • Focus on areas of highest uncertainty or information gain

  • Validate computational predictions experimentally

• Data Integration for Improved Characterization:

  • Combine structural prediction, sequence analysis, and experimental binding data

  • Utilize Absolut! simulation framework to evaluate out-of-distribution performance

  • Implement ensemble approaches combining multiple prediction algorithms

• Experimental Design Optimization:

  • Use predictions to guide epitope selection for new antibodies

  • Optimize testing protocols based on computational predictions

  • Reduce experimental iterations through improved prediction accuracy

How can non-specific binding of MTHFSD antibodies be mitigated in experimental applications?

When encountering non-specific binding with MTHFSD antibodies:

• Antibody Selection and Validation:

  • Choose antibodies targeting unique epitopes (e.g., N-terminal region, amino acids 43-69)

  • Verify purity through affinity purification methods (protein A column followed by peptide affinity purification)

  • Test multiple antibodies from different sources when possible

• Protocol Optimization:

  • Titrate antibody concentrations (typically 1:200-1:500 for IHC, 1:1000 for WB)

  • Extend blocking time (minimum 1 hour at room temperature)

  • Use alternative blocking agents (5% BSA, commercial blocking buffers)

  • Include 0.05% Tween-20 in wash buffers

• Peptide Competition Controls:

  • Pre-incubate antibody with immunizing peptide

  • Compare staining patterns with and without peptide blocking

  • Use commercially available blocking peptides when possible

• Technical Considerations:

  • Ensure proper antigen retrieval (TE9 buffer, pH 9.0, 110°C for 15 min)

  • Store antibodies appropriately (-20°C, avoid freeze/thaw cycles)

  • Include appropriate negative controls in each experiment

What are the most common challenges when using MTHFSD antibodies in ALS research models?

Researchers working with MTHFSD in ALS models face several specific challenges:

• Altered Protein Expression and Localization:

  • Nuclear vs. cytoplasmic distribution changes require careful interpretation

  • Downregulation in disease models may require more sensitive detection methods

  • Presence in stress granules necessitates co-localization studies

• Model-Specific Considerations:

  • Mouse models: Temporal changes (no difference at 5 months, significant at 10 months in TDP-43A315T mice)

  • Human samples: Variable disease stages and subtypes (C9ORF72, non-C9ORF72, SOD1)

  • Cell models: Different stress induction protocols may yield variable results

• Technical Considerations:

  • Tissue preservation: Rapid fixation critical for maintaining cellular architecture

  • Background autofluorescence: Especially problematic in aged tissues

  • Cross-reactivity with other RNA-binding proteins in stress granules

• Data Interpretation:

  • Distinguish between cause and consequence of pathology

  • Account for cell-type specific changes (motor neurons vs. glia)

  • Consider temporal dynamics of disease progression

What emerging technologies might enhance MTHFSD research beyond current antibody-based approaches?

Several emerging technologies offer promising avenues for advancing MTHFSD research:

• CRISPR-Based Tagging:

  • Endogenous tagging of MTHFSD with fluorescent proteins or epitope tags

  • Live-cell tracking of protein dynamics

  • Tissue-specific or inducible expression systems

• Single-Cell Analysis Technologies:

  • Single-cell RNA-seq to identify cell-type specific expression patterns

  • Spatial transcriptomics to map MTHFSD expression in tissue context

  • Mass cytometry for high-dimensional protein profiling

• Proximity Labeling Approaches:

  • BioID or APEX2 fusion proteins to identify proximal interactors

  • Spatial mapping of MTHFSD interaction networks

  • Identification of transient or context-specific interactions

• Structural Biology Techniques:

  • Cryo-EM for structural determination of MTHFSD complexes

  • Hydrogen-deuterium exchange mass spectrometry for dynamic structural analysis

  • Integrative structural modeling combining multiple data types

• Computational Approaches:

  • Machine learning for predicting functional impacts of mutations

  • Network analysis to position MTHFSD within broader disease pathways

  • Active learning strategies to optimize experimental design

How might understanding MTHFSD's role in stress granules inform therapeutic approaches?

MTHFSD's identification as a stress granule component has important therapeutic implications:

• Targeting Stress Granule Dynamics:

  • Compounds modulating stress granule assembly/disassembly

  • Small molecules targeting MTHFSD-RNA interactions

  • Peptide inhibitors of protein-protein interactions within stress granules

• Restoring Nuclear Localization:

  • Nuclear import enhancers

  • Compounds preventing nuclear export

  • Targeting pathways regulating MTHFSD localization

• RNA Metabolism Modulation:

  • RNA stabilization approaches

  • Translation regulators

  • Splicing modulators for MTHFSD targets

• Combinatorial Approaches:

  • Targeting multiple stress granule components simultaneously

  • Combining MTHFSD-targeted approaches with TDP-43 modulators

  • Pathway-based interventions addressing broader RNA metabolism defects

• Considerations for Therapeutic Development:

  • Need for cell-type specific delivery methods

  • Temporal requirements (early vs. late intervention)

  • Potential for compensatory mechanisms