Recombinant Inositol-3-phosphate synthase (ino1)

What is Inositol-3-phosphate synthase (Ino1) and what is its primary function?

Inositol-3-phosphate synthase (Ino1) is the key biosynthetic enzyme responsible for inositol production in cells. Its primary function is catalyzing the conversion of glucose-6-phosphate to inositol-3-phosphate, which is the rate-limiting step in de novo inositol biosynthesis. Research has demonstrated that Ino1 plays a critical role in maintaining cellular inositol levels, which are altered in various disorders, including bipolar disorder and Alzheimer's disease . Beyond its biosynthetic role, recent findings indicate that Ino1 has additional functions independent of inositol production that significantly impact cellular metabolism.

How does Ino1 deletion affect cellular inositol levels?

Deletion of the ino1 gene creates an inositol auxotrophic phenotype where cells become dependent on exogenous inositol for survival. Studies in Dictyostelium discoideum have shown that ino1- cells grown with inositol supplementation maintain intermediate inositol levels (1.8 ± 0.1 μM), which significantly decrease to 0.8 ± 0.1 μM following 12 hours of exogenous inositol removal . This represents a rapid 56% reduction in inositol levels. Interestingly, even with inositol supplementation, ino1- mutants cannot achieve the same elevated inositol levels as wild-type cells with supplementation (3.4 ± 0.1 μM), suggesting that the complete restoration of normal inositol homeostasis requires the presence of the Ino1 enzyme itself .

What model organisms are commonly used to study Ino1 function?

Dictyostelium discoideum has emerged as a valuable model organism for investigating Ino1 function. This simple eukaryote offers several advantages for Ino1 studies, including genetic tractability, well-characterized cellular processes, and conservation of many metabolic pathways relevant to higher organisms. In Dictyostelium, researchers have successfully created ino1- mutants through targeted gene deletion, providing a powerful tool to distinguish between the effects of Ino1 loss and inositol depletion . Other model systems used in Ino1 research include yeast (Saccharomyces cerevisiae), which has contributed significantly to our understanding of inositol metabolism, and various mammalian cell lines for studies more directly relevant to human health.

What are the most effective methods for generating recombinant Ino1 for in vitro studies?

For generating functional recombinant Ino1, the following methodological approach has proven effective:

• Expression System Selection: E. coli BL21(DE3) strains are commonly used for high-yield expression of Ino1. For studies requiring post-translational modifications, insect cell systems (Sf9 or High Five cells) using baculovirus vectors are recommended.

• Construct Design: Include a His-tag or GST-tag for purification, with an optimal construct incorporating a precision protease cleavage site between the tag and Ino1 sequence. When designing your construct, consider:

  • Adding a flexible linker (GSGSGS) between the tag and protein

  • Codon optimization for the expression system

  • Removal of potential internal restriction sites

• Purification Protocol: A two-step purification typically yields high-purity Ino1:

  • Initial affinity chromatography (Ni-NTA for His-tagged proteins)

  • Size exclusion chromatography to remove aggregates and ensure homogeneity

• Activity Verification: Confirm enzymatic activity using a coupled spectrophotometric assay measuring NADH oxidation in the presence of glucose-6-phosphate.

For tagged constructs specifically used in binding partner identification experiments, research has demonstrated success with both RFP and GFP fusion proteins, as evidenced by their effective use in coimmunoprecipitation studies identifying Ino1 binding partners .

How can researchers effectively measure changes in cellular inositol levels following genetic manipulation of Ino1?

Accurate measurement of cellular inositol levels following Ino1 manipulation requires:

• NMR Spectroscopy Method:

  • Extract metabolites using a chloroform/methanol/water mixture (1:3:1 ratio)

  • Analyze samples using 1H-NMR spectroscopy

  • Quantify inositol peaks using standard curves with known concentrations

• Sample Preparation Considerations:

  • Maintain consistent cell numbers (typically 5 × 107 cells per sample)

  • Quick quenching of metabolism (liquid nitrogen)

  • Rigorous extraction procedures to ensure complete metabolite recovery

• Controls and Validation:

  • Include wild-type cells grown with and without inositol supplementation

  • Use known inositol standards for calibration

  • Consider multiple time points following inositol removal (e.g., 12h, 24h)

This methodological approach has been validated in Dictyostelium studies where wild-type cells contained 1.5 ± 0.1 μM inositol (unsupplemented) versus 3.4 ± 0.1 μM following inositol supplementation, while ino1- cells showed dynamic changes from 1.8 ± 0.1 μM (with supplementation) to 0.8 ± 0.1 μM (12h after removal) .

What techniques are available for identifying Ino1 binding partners?

For comprehensive identification of Ino1 binding partners, a multi-phase approach is recommended:

• Coimmunoprecipitation with Mass Spectrometry:

  • Express Ino1-RFP or Ino1-GFP fusion proteins in cells

  • Lyse cells under non-denaturing conditions

  • Immunoprecipitate using anti-RFP/GFP antibody-coated agarose beads

  • Separate proteins by SDS-PAGE and identify by mass spectrometry

• Validation of Specific Interactions:

  • Co-express Ino1 with candidate binding partners tagged with different epitopes (e.g., FLAG)

  • Perform reciprocal coimmunoprecipitation experiments

  • Confirm interactions by Western blotting with appropriate antibodies

• Functional Verification:

  • Assess the biological relevance of identified interactions

  • Determine whether the interaction depends on catalytic activity

  • Map interaction domains using truncation mutants

Using this approach, researchers have identified 104 potential Ino1 binding partners across six major functional groups: actin-related, immunity/stress-related, metabolism, nucleic acid-related, protein catabolism/modification, and transport proteins . Notable confirmed binding partners include Q54IX5, a protein containing SEL1L1 domains with homology to macromolecular complex adaptor proteins .

How can researchers differentiate between phenotypes caused by inositol depletion versus those resulting from Ino1 protein loss?

Differentiating between these two phenomena requires a carefully designed experimental approach:

• Experimental Groups Design:

  Group	Genotype	Inositol Status	Purpose
  1	Wild-type	No supplementation	Baseline control
  2	Wild-type	With supplementation	Control for inositol effects
  3	ino1-	With supplementation	Isolates Ino1 protein loss effects
  4	ino1-	Removal after supplementation	Combined effects
  5	ino1- with ino1-RFP	No supplementation	Genetic rescue control

• Multi-parameter Phenotypic Analysis:

  • Examine cellular morphology and polarization

  • Measure cell movement parameters (velocity, aspect, directness)

  • Assess cytokinesis using nuclear staining

  • Evaluate substrate adhesion

  • Analyze autophagy markers

• Metabolic Profiling:

  • Perform principal component analysis of metabolic profiles

  • Identify metabolites specifically altered by Ino1 loss versus inositol depletion

  • Examine catabolic versus anabolic markers

Research using this approach has revealed distinct phenotypes: Ino1 loss specifically affects cell shape (regardless of inositol supplementation), while inositol depletion primarily affects cell velocity, substrate adhesion, and cytokinesis . Metabolically, Ino1 loss causes broad changes accounting for 53% of total metabolic variance, while inositol depletion accounts for only 12% of variance .

What are the non-biosynthetic functions of Ino1 and how do they impact cellular metabolism?

The non-biosynthetic functions of Ino1 represent a significant area of ongoing research. Current evidence suggests:

• Metabolic Regulation Independent of Inositol Levels:

  • Ino1 loss triggers a shift to catabolic metabolism that is not rescued by inositol supplementation

  • This shift includes increased levels of:

    • Amino acids (alanine, aspartate, glycine, GABA, isoleucine, lysine, methionine)

    • Energy-related metabolites (fumarate, lactate)

    • Phosphorylated adenosine derivatives (5′AMP, 3′AMP, ATP, cAMP)

    • sn-glycero-3-phosphocholine (GPC)

• Protein-Protein Interactions:

  • Ino1 interacts with proteins involved in diverse cellular processes

  • Strong binding to Q54IX5, a protein with SEL1-like repeats that may function as an adaptor in macromolecular complexes

  • Potential weak interaction with GpmA, a phosphoglycerate mutase

• Potential Cellular Function Regulation:

  • Evidence suggests Ino1 may be involved in proton transport mechanisms through interaction with V-type proton ATPase catalytic subunits

  • Association with actin-related proteins suggests potential cytoskeletal regulatory functions

These non-biosynthetic functions appear to position Ino1 as a metabolic regulator beyond its enzymatic role in inositol production, potentially serving as a sensing or scaffolding component in metabolic regulation networks .

How does Ino1 activity correlate with phosphoinositide metabolism and signaling pathways?

The relationship between Ino1 activity and phosphoinositide metabolism involves complex regulatory mechanisms:

• Inositol Availability and Phosphoinositide Levels:

  • Inositol depletion in ino1- cells causes a substantial decrease in phosphoinositide levels

  • This effect can be rescued by inositol supplementation, indicating direct dependence of phosphoinositide synthesis on inositol availability

• Signaling Pathway Impact:

  • Changes in phosphoinositide levels affect multiple signaling cascades:

    • Cell movement and chemotaxis (PI3K pathway)

    • Vesicular trafficking (PI4K pathway)

    • Stress responses (PLC pathway)

• Regulatory Feedback Mechanisms:

  • Phosphoinositide levels may influence Ino1 activity through:

    • Direct allosteric regulation

    • Indirect effects via interacting proteins

    • Transcriptional control of ino1 expression

Research shows that while inositol supplementation can restore phosphoinositide levels in ino1- cells, the broader metabolic changes caused by Ino1 loss persist, suggesting complex regulatory relationships beyond simple substrate availability .

What are common challenges in maintaining viable ino1 knockout cell lines and how can they be addressed?

Maintaining viable ino1 knockout cell lines presents several challenges:

• Inositol Supplementation Optimization:

  Inositol Concentration	Advantages	Disadvantages
  500 μM	Optimal growth support	Masks some phenotypes
  200 μM	Balance between growth and phenotype	Requires frequent media changes
  100 μM	More pronounced phenotypes	Growth limitations

• Media Considerations:

  • Use chemically defined media to control inositol levels precisely

  • Supplement with myo-inositol (not D-chiro-inositol or other isomers)

  • Consider inositol carried over from serum in mammalian cell culture

• Growth Monitoring Protocol:

  • Maintain cells at sub-confluent densities

  • Monitor for multinucleated cells (indicator of cytokinesis defects)

  • Check for changes in substrate adhesion

  • Implement regular viability assessments

• Genetic Stability Solutions:

  • Maintain master stocks with high inositol supplementation

  • Limit passage numbers for experimental cultures

  • Consider inducible knockout systems for long-term studies

Research has shown that ino1- cells exhibit decreased adhesion and altered cytokinesis (24.7% of cells accumulate ≥3 nuclei under inositol depletion versus 7.7% in wild-type) , making these parameters important indicators of cell line health.

How can researchers control for artifacts in metabolic profiling experiments involving Ino1 manipulation?

Controlling for artifacts in metabolic profiling experiments requires:

• Experimental Design Controls:

  • Include time-matched samples for all conditions

  • Use biological triplicates minimum for each condition

  • Implement technical replicates for extraction and analysis

• Sample Preparation Standardization:

  • Harvest cells at consistent density and growth phase

  • Quench metabolism rapidly using cold extraction methods

  • Normalize metabolite concentrations to cell number or protein content

• Analytical Quality Controls:

  • Include a quality control sample pool run periodically throughout analysis

  • Add internal standards covering different metabolite classes

  • Run blank samples to identify potential contaminants

• Data Analysis Considerations:

  • Apply appropriate normalization methods

  • Use supervised and unsupervised multivariate analyses (PCA, OPLS)

  • Validate findings with targeted metabolite analysis

Studies have demonstrated that Ino1 loss accounts for 53% of metabolic variance while inositol depletion accounts for only 12% , highlighting the importance of distinguishing these effects through careful experimental design and controls.

What are promising approaches for investigating the structural dynamics of Ino1 enzyme during catalysis?

Advanced structural biology techniques offer promising approaches for studying Ino1 dynamics:

• Cryo-Electron Microscopy (Cryo-EM):

  • Enables visualization of Ino1 in different conformational states

  • Can potentially capture the enzyme during catalytic steps

  • Allows study of Ino1 in complex with binding partners

• Hydrogen-Deuterium Exchange Mass Spectrometry (HDX-MS):

  • Maps regions of conformational flexibility during catalysis

  • Identifies potential allosteric sites

  • Provides insights into how binding partners influence Ino1 structure

• Time-Resolved X-ray Crystallography:

  • Captures structural snapshots during the catalytic cycle

  • Requires stable crystal forms amenable to substrate soaking

  • Can be combined with light-activated caged substrates

• Molecular Dynamics Simulations:

  • Predicts conformational changes during substrate binding and catalysis

  • Explores effects of mutations on enzyme dynamics

  • Models interactions with binding partners at atomic resolution

These approaches can help elucidate how Ino1's structural dynamics contribute to both its catalytic function and its non-biosynthetic roles in metabolic regulation.

How might understanding Ino1's non-biosynthetic functions lead to new therapeutic approaches for disorders involving inositol dysregulation?

The therapeutic potential of targeting Ino1's non-biosynthetic functions includes:

• Novel Drug Target Identification:

  • Specific protein-protein interactions (e.g., Ino1-Q54IX5) could be targeted

  • Allosteric modulators might selectively affect non-biosynthetic functions

  • Small molecules disrupting or enhancing specific interactions could have therapeutic value

• Metabolic Pathway Modulation:

  • Targeting the catabolic shift associated with Ino1 loss

  • Modulating amino acid metabolism altered in Ino1 deficiency

  • Addressing energy metabolism changes through parallel pathways

• Biomarker Development:

  • Metabolic signatures of Ino1 dysfunction could serve as diagnostic indicators

  • Monitoring treatment efficacy through metabolic profiling

  • Identifying patient subgroups most likely to benefit from inositol-based interventions

• Precision Medicine Applications:

  • Genetic screening for variations in Ino1 and interacting partners

  • Tailored interventions based on specific defects in inositol metabolism

  • Combination therapies addressing both biosynthetic and non-biosynthetic functions

Research has identified 104 potential Ino1 binding partners across various functional categories , providing multiple avenues for therapeutic intervention beyond simply supplementing inositol.

What experimental approaches could further elucidate the regulatory relationship between Ino1 and its identified binding partners?

To further characterize Ino1's interactions with binding partners:

• Proximity-Based Protein Interaction Mapping:

  • BioID or APEX2 proximity labeling with Ino1 as the bait

  • Split-BioID to identify condition-specific interactions

  • Quantitative SILAC-based proximity labeling to measure interaction dynamics

• Domain Mapping and Functional Analysis:

  • Generate truncated Ino1 constructs to identify binding domains

  • Use site-directed mutagenesis to disrupt specific interactions

  • Assess functional consequences of disrupting each interaction

• Spatiotemporal Interaction Dynamics:

  • Live-cell imaging with fluorescently tagged proteins

  • FRET/BRET assays to measure real-time interactions

  • Optogenetic approaches to control interactions with temporal precision

• Systems Biology Integration:

  • Correlate interaction data with metabolomic profiles

  • Generate network models of Ino1-centered protein interactions

  • Implement perturbation experiments to validate network predictions

The strong interaction between Ino1 and Q54IX5 (a protein with SEL1L1 domains homologous to macromolecular complex adaptor proteins) represents a particularly promising starting point for these investigations.