MTHSFD Human

What is MTHSFD protein and what are its key structural characteristics?

MTHSFD (Methenyltetrahydrofolate synthase domain-containing protein) is a human protein containing 383 amino acids in its native form. When produced as a recombinant protein, it typically includes additional amino acids such as a His-tag, resulting in approximately 406 amino acids with a molecular mass of 44.5kDa . The protein's structure includes specific domains characteristic of methenyltetrahydrofolate synthase activity, which plays a role in folate metabolism.

Key structural features include:

• Single, non-glycosylated polypeptide chain

• Functional domains involved in nucleic acid and nucleotide binding

• When expressed recombinantly, often includes a 23 amino acid His-tag at the N-terminus to facilitate purification

The amino acid sequence of the recombinant form includes specific motifs that contribute to its enzymatic function and protein interactions, which are critical for its biological activity in folate metabolism pathways.

What expression systems are typically used for MTHSFD protein production?

MTHSFD human recombinant protein is commonly produced in Escherichia coli expression systems. The typical production process involves:

• Cloning the human MTHSFD gene (coding for amino acids 1-383) into an expression vector

• Adding tags (commonly a His-tag) to facilitate purification

• Transforming the construct into E. coli

• Inducing protein expression under controlled conditions

• Cell lysis and protein extraction

• Purification using chromatographic techniques that leverage the His-tag

• Formulation in a stabilizing buffer containing components such as glycerol, DTT, and phosphate-buffered saline

The resulting purified protein typically achieves >90% purity as determined by SDS-PAGE analysis and can be stored at 4°C for short-term use (2-4 weeks) or at -20°C with a carrier protein (0.1% HSA or BSA) for longer-term storage .

What are the primary research applications of MTHSFD protein?

MTHSFD protein is utilized in several research contexts:

• Biochemical pathway studies: Investigating folate metabolism pathways and one-carbon transfer reactions

• Structural biology: Examining protein folding, domain organization, and active site configuration

• Genetic disease research: Studying the relationship between MTHSFD variants and associated conditions including:

  • Pancreatic, annular, and gastrointestinal defects

  • Immunodeficiency syndromes

• Protein interaction studies: Identifying binding partners and regulatory proteins that interact with MTHSFD

• Enzyme kinetics: Characterizing the catalytic properties and substrate specificity

These applications collectively contribute to understanding MTHSFD's role in normal cellular function and disease pathogenesis, particularly in contexts where folate metabolism impacts development, immune function, and cellular homeostasis.

How should researchers design experiments to investigate MTHSFD's role in folate metabolism?

Designing robust experiments to investigate MTHSFD's role in folate metabolism requires careful consideration of multiple factors:

Experimental Framework Design:

• Hypothesis formulation: Develop specific, testable hypotheses about MTHSFD's function within folate metabolism pathways

• Model selection: Choose appropriate in vitro, cellular, or animal models based on the specific aspect of MTHSFD function being investigated

• Intervention design: Plan genetic manipulations (e.g., CRISPR/Cas9), pharmacological interventions, or environmental modifications

• Outcome measurement: Select appropriate molecular, cellular, or physiological parameters to evaluate

Study Design Approaches:

What methodological challenges exist in studying MTHSFD protein interactions?

Studying MTHSFD protein interactions presents several methodological challenges that researchers must address:

• Transient interactions: MTHSFD likely forms dynamic complexes with other folate metabolism enzymes that can be difficult to capture using traditional interaction methods.

• Structural conformations: The protein may adopt different conformations depending on binding partners, substrates, or cellular conditions, requiring methods that can detect these state-dependent interactions.

• Technical considerations:

  • Ensuring protein functionality after tagging for pull-down assays

  • Maintaining physiological conditions during interaction studies

  • Distinguishing direct from indirect interactions in complex networks

  • Validating interactions identified in high-throughput screens

• In vivo relevance: Interactions identified in vitro may not accurately reflect those occurring in cellular environments with appropriate compartmentalization and local concentrations.

Addressing these challenges requires combining multiple complementary approaches, such as:

• Co-immunoprecipitation with specific antibodies

• Proximity labeling techniques (BioID, APEX)

• FRET/BRET for real-time interaction monitoring

• Crosslinking mass spectrometry for structural interface mapping

• Hydrogen-deuterium exchange mass spectrometry for conformational changes

Validation across different experimental systems is essential to establish confidence in identified interaction partners .

How do mutations in MTHSFD affect protein function and contribute to disease phenotypes?

Mutations in MTHSFD can disrupt protein function through multiple mechanisms that ultimately contribute to disease phenotypes:

Functional Impact Mechanisms:

• Catalytic activity disruption: Mutations in the active site can directly impair enzymatic function, reducing the conversion of folate intermediates.

• Protein stability alterations: Some mutations may decrease protein half-life through increased degradation, reducing effective intracellular concentrations.

• Subcellular mislocalization: Mutations affecting localization signals may prevent MTHSFD from reaching its functional compartment.

• Protein-protein interaction disruption: Alterations at binding interfaces can prevent formation of essential metabolic complexes.

Disease Phenotype Connections:

The connection between MTHSFD mutations and conditions such as pancreatic/gastrointestinal defects and immunodeficiency syndromes likely involves:

• Developmental impacts: Disrupted folate metabolism can affect rapidly dividing cells during development, potentially explaining gastrointestinal and pancreatic abnormalities.

• Immune cell dysfunction: Impaired folate metabolism may compromise lymphocyte proliferation and function, contributing to immunodeficiency phenotypes.

• Methylation defects: Altered folate metabolism can impact methylation reactions, potentially affecting gene expression patterns during development and in mature tissues.

Investigating these connections requires integrating genetic analysis of patient variants with functional studies in appropriate model systems to establish causality and elucidate mechanisms .

What experimental controls are essential for validating MTHSFD functional studies?

Biochemical Assay Controls:

• Negative controls:

  • Heat-inactivated MTHSFD enzyme preparations

  • Reaction mixtures lacking MTHSFD enzyme

  • Competitive inhibitor inclusion to validate specificity

• Positive controls:

  • Commercial or validated MTHSFD preparations with known activity

  • Parallel reactions with well-characterized related enzymes

• Technical controls:

  • Multiple protein batches to account for preparation variability

  • Standard curves for all quantitative measurements

  • Internal standards for metabolite quantification

Cellular and In Vivo Controls:

• Expression controls:

  • Empty vector transfections for overexpression studies

  • Non-targeting siRNA/sgRNA for knockdown/knockout experiments

  • Wild-type MTHSFD rescue experiments in knockout models

• Specificity controls:

  • Multiple independent siRNA/sgRNA sequences targeting MTHSFD

  • Dose-response relationships in overexpression/inhibition studies

  • Complementary genetic and pharmacological approaches

• Experimental design controls:

  • Blinded analysis to prevent observer bias

  • Randomization of sample processing order

  • Inclusion of multiple biological replicates

These controls should be systematically incorporated into experimental designs following established human factors experimental design principles to ensure scientific rigor and reproducibility .

What statistical approaches are most appropriate for analyzing MTHSFD research data?

Selecting appropriate statistical approaches for MTHSFD research requires consideration of experimental design, data types, and research questions:

For Biochemical Studies:

• Enzyme kinetics: Non-linear regression for Michaelis-Menten parameters (Km, Vmax)

• Dose-response relationships: EC50/IC50 determination using appropriate curve fitting

• Comparative activity: ANOVA with post-hoc tests for comparing wild-type vs. mutant forms

For Cell and Tissue Studies:

• Expression analysis:

  • t-tests (two conditions) or ANOVA (multiple conditions) for normally distributed data

  • Non-parametric alternatives (Mann-Whitney, Kruskal-Wallis) for non-normal distributions

• Correlation analysis: Pearson or Spearman correlation between MTHSFD levels and phenotypic measures

• Time-course experiments: Repeated measures ANOVA or mixed-effects models

Advanced Statistical Considerations:

• Sample size determination: Power analysis to calculate required sample sizes for desired effect detection

• Multiple testing correction: Bonferroni, Benjamini-Hochberg, or other FDR methods when performing multiple comparisons

• Experimental design factors: Consider blocking, stratification, or covariate inclusion to control for confounding variables

Following human factors experimental design principles, researchers should:

• Select statistical tests based on data distribution and experimental design

• Report effect sizes alongside p-values

• Include confidence intervals where appropriate

• Distinguish between exploratory and confirmatory analyses

How can researchers effectively integrate multi-omics approaches to study MTHSFD function?

Integrating multi-omics approaches provides comprehensive insights into MTHSFD function across biological scales:

Core Omics Platforms Relevant to MTHSFD Research:

• Genomics: Identify genetic variants and their functional impacts

• Transcriptomics: Measure expression changes in MTHSFD and related pathway genes

• Proteomics: Analyze protein levels, post-translational modifications, and interactions

• Metabolomics: Assess folate metabolites and related pathway components

Integration Strategies:

Implementation Workflow:

• Experimental design with consistent perturbations across platforms

• Coordinated sample collection to minimize technical variability

• Platform-specific analyses followed by integration methods

• Hypothesis generation and validation using targeted approaches

This integrated approach allows researchers to connect MTHSFD genetic variations to molecular phenotypes and ultimately to cellular and organismal functions, providing a systems-level understanding of this protein's role in health and disease .

What are the key considerations for designing MTHSFD knockout or knockdown studies?

Designing effective MTHSFD genetic manipulation studies requires careful consideration of multiple factors:

Selection of Genetic Manipulation Approach:

Experimental Design Considerations:

• Validation strategy:

  • Confirm target reduction at mRNA and protein levels

  • Use multiple independent siRNAs/sgRNAs to control for off-target effects

  • Include rescue experiments with wildtype MTHSFD

• Control selection:

  • Non-targeting siRNA/sgRNA with similar chemistry

  • Wildtype cells processed in parallel

  • Rescue controls to establish specificity

• Phenotypic analysis:

  • Select endpoints relevant to folate metabolism (e.g., nucleotide synthesis, methylation)

  • Include both proximal (direct pathway metabolites) and distal (cellular phenotypes) measures

  • Consider time-dependent effects, especially for acute knockdown approaches

Following human factors experimental design principles, researchers should systematically document all experimental variables and include appropriate controls to enable robust interpretation of results .

How should researchers approach the structural and functional characterization of MTHSFD mutants?

Comprehensive characterization of MTHSFD mutants requires integrating structural and functional approaches:

Mutant Selection Strategy:

• Disease-associated variants: Prioritize mutations identified in patients with relevant conditions

• Conserved domain mutations: Target residues in functional domains based on sequence conservation

• Structure-guided mutations: Use computational predictions or structural data to select functionally important residues

• Systematic scanning: Alanine scanning or domain deletion approaches for comprehensive functional mapping

Structural Characterization:

• X-ray crystallography or cryo-EM for high-resolution structure determination

• Circular dichroism spectroscopy to assess secondary structure stability

• Thermal shift assays to measure protein stability changes

• Hydrogen-deuterium exchange mass spectrometry to identify conformational differences

• Molecular dynamics simulations to predict mutation effects on protein dynamics

Functional Characterization:

• Enzyme kinetics to measure catalytic parameters (Km, Vmax, kcat)

• Substrate specificity profiling to detect altered preferences

• Protein interaction studies to identify disrupted binding partners

• Cellular localization to assess trafficking or compartmentalization changes

• Complementation assays in knockout models to test functional rescue capacity

Integrated Analysis Framework:

• Correlate structural changes with functional consequences

• Map mutations onto protein structure to identify functional hotspots

• Group mutants by mechanism (e.g., stability, catalysis, interaction)

• Connect biochemical defects to cellular phenotypes

This comprehensive approach enables classification of mutations by mechanism and severity, providing insights into structure-function relationships and potential therapeutic approaches for disease-associated variants .

What approaches are recommended for studying MTHSFD in different tissue contexts?

Understanding MTHSFD function across different tissues requires specialized approaches that account for tissue-specific contexts:

Tissue-Specific Expression Analysis:

• Transcriptomics databases: Utilize public databases (GTEx, Human Protein Atlas) to establish baseline tissue expression patterns

• Immunohistochemistry: Validate protein expression in tissue sections using specific antibodies

• Single-cell RNA-seq: Determine cell type-specific expression within heterogeneous tissues

• Tissue western blots: Quantify relative protein levels across multiple tissues

Tissue Context Experimental Models:

Tissue-Specific Functional Assessment:

• Metabolomics: Compare folate metabolite profiles across tissues

• Tissue-specific phenotyping: Assess tissue-relevant endpoints (e.g., proliferation in immune cells, methylation in neurons)

• Ex vivo tissue explants: Analyze MTHSFD function in intact tissue architecture

• Tissue-specific rescue: Restore MTHSFD expression in specific tissues of knockout models

Considerations for Tissue Differences:

• Proliferation rates affecting folate metabolism demands

• Tissue-specific interaction partners

• Differential expression of redundant or complementary enzymes

• Tissue-specific isoforms or post-translational modifications

These approaches collectively enable researchers to understand how MTHSFD function is adapted to different tissue environments and metabolic demands.

How should researchers approach contradictory findings in MTHSFD literature?

Reconciling contradictory findings is an essential aspect of advancing MTHSFD research. A systematic approach includes:

Sources of Contradiction Analysis:

• Methodological differences:

  • Expression systems (E. coli vs. mammalian cells)

  • Assay conditions (buffer composition, temperature, pH)

  • Detection methods (direct vs. coupled assays)

  • Protein constructs (full-length vs. truncated, tag position)

• Biological variables:

  • Cell/tissue types with different metabolic contexts

  • Species differences in protein function

  • Presence of regulatory factors in different systems

  • Compensatory mechanisms in knockout models

Resolution Strategy:

Framework for Interpretation:

• Consider that contradictions may reflect true biological complexity rather than error

• Develop testable hypotheses that could explain context-dependent functions

• Report detailed methods to enable reproducibility by others

• Acknowledge limitations and alternative interpretations

Following human factors experimental design principles, researchers should systematically document and test variables that might explain contradictory findings .

What are best practices for data sharing and reproducibility in MTHSFD research?

Ensuring reproducibility and facilitating data sharing are essential for advancing MTHSFD research:

Data Sharing Recommendations:

• Sequence and structural data:

  • Deposit DNA constructs in repositories (Addgene)

  • Submit protein structures to Protein Data Bank (PDB)

  • Share recombinant protein production protocols in detail

• Experimental data:

  • Provide raw data in supplementary materials or repositories

  • Use standardized formats for different data types

  • Include detailed metadata describing experimental conditions

• Analytical methods:

  • Share analysis code and scripts in repositories (GitHub)

  • Document software versions and parameters

  • Provide detailed statistical analysis protocols

Reproducibility Best Practices:

• Method documentation: Provide sufficient detail for others to replicate experiments, including:

  • Exact buffer compositions

  • Specific reagent sources and catalog numbers

  • Detailed equipment settings

  • Step-by-step protocols with timing information

• Internal validation:

  • Perform technical and biological replications

  • Use multiple methodological approaches

  • Include positive and negative controls in all experiments

• Open science practices:

  • Preregister study designs where applicable

  • Publish negative results

  • Consider publishing protocols in dedicated journals

• Quality control measures:

  • Validate key reagents (antibody specificity, enzyme activity)

  • Include authentication of cell lines

  • Report all exclusion criteria and data filtering steps

Following these practices enhances research quality, accelerates scientific progress, and builds confidence in findings related to MTHSFD structure and function.