C9ORF103 Human

What is C9ORF103 and what is its primary function in human metabolism?

C9ORF103 (Chromosome 9 open reading frame 103) is a human gene that encodes gluconokinase (GNTK), an enzyme that catalyzes the ATP-dependent phosphorylation of gluconate to 6-phospho-D-gluconate. This enzyme plays a critical role in the initial step of gluconate catabolism in humans . It belongs to the gluconokinase gntK/gntV family and functions in carbohydrate acid metabolism, specifically in D-gluconate degradation pathways . While direct oxidation of glucose to generate gluconate is generally not perceived to take place in humans, the presence of functional human gluconokinase implies a potential carbon flux route into the hexose monophosphate shunt (HMS), which could have significant implications for human energy metabolism .

What are the known isoforms of C9ORF103 and how do they differ functionally?

Two isoforms of C9ORF103 have been identified (isoforms I and II). In vitro assays of these isoforms expressed in human HeLa cell lysates demonstrated that only isoform I possesses ATP-dependent phosphorylation activity. This functional difference is consistent with the absence of a phosphate binding loop domain in isoform II . Isoform I shows approximately 35% sequence similarity to both gluconokinases encoded within the E. coli genome. A defining structural difference is an 18 amino acid insert that is found in various NMP kinases with similar protein structure to E. coli GntK .

How does human C9ORF103/GNTK differ from bacterial gluconokinases?

Human GNTK can be distinguished by its unique structural elements and substrate specificity profile. While maintaining the core enzymatic function of gluconate phosphorylation, human GNTK shows distinct evolutionary adaptations compared to its bacterial counterparts .

What are the most effective methods for expression and purification of recombinant human C9ORF103?

The most effective approach for expression and purification of recombinant human C9ORF103 involves:

• Expression System Selection: E. coli expression systems (particularly Rosetta cells) have proven effective for producing functional human C9ORF103 .

• Vector Construction: Cloning C9ORF103 isoform I into an expression vector (such as pCPR0011) downstream of affinity tags (like 6xHIS and STREPII tags) facilitates later purification .

• Culture Conditions: Optimal expression involves:

  • Using TB media with appropriate antibiotics (kanamycin at 50 μg/ml, chloramphenicol at 25 μg/ml for selection)

  • Induction with 0.5 mM IPTG at appropriate cell density

  • Incubation at 37°C with shaking at 180 rpm

• Purification Strategy: Proprietary chromatographic techniques following affinity tag-based purification yield protein with greater than 95% purity as determined by SDS-PAGE .

The resulting protein product typically contains 211 amino acids (including the 187 amino acids of the native protein plus 24 amino acids from the His-tag at the N-terminus) with a molecular mass of approximately 23.1 kDa .

How can researchers verify the identity and activity of purified C9ORF103/GNTK?

A comprehensive verification protocol for C9ORF103/GNTK should include:

• SDS-PAGE Analysis: Confirms protein purity (>95%) and approximate molecular weight (observed: ~21.8 kDa; calculated: 23.3 kDa) .

• Mass Spectrometry: Verifies the exact molecular mass (reported as 23,325.2 Da for tagged protein) .

• Native Gel Electrophoresis: Determines oligomerization state (C9ORF103 exists as dimers and higher oligomers) .

• Immunoblotting: Confirmation using polyclonal antibodies against human GNTK, which should cross-react with both human and E. coli GNTK .

• Enzymatic Activity Assay: Measuring ATP-dependent phosphorylation of gluconate to 6-phospho-D-gluconate. Activity can be detected spectrophotometrically by monitoring the drop in ATP concentration following incubation with substrates .

• Substrate Specificity Testing: Screening against a metabolic library to confirm specific activity toward gluconate and glucono-1,5-lactone, with minimal activity toward other metabolites .

What experimental conditions yield optimal enzymatic activity for C9ORF103/GNTK assays?

For optimal enzymatic activity, C9ORF103/GNTK assays should consider:

• Buffer Composition: 20 mM Tris-HCl buffer (pH 8.0) containing 100 mM NaCl, 1 mM DTT, and 20% glycerol has been demonstrated to maintain protein stability and activity .

• Substrate Concentration: Gluconate or glucono-1,5-lactone should be used as primary substrates, as C9ORF103 shows high specificity for these compounds over other potential substrates, including carbohydrates/carbohydrate conjugates and organic acids .

• ATP Requirements: The assay requires ATP as a phosphate donor, and activity can be measured by monitoring ATP depletion .

• Temperature and pH: While specific optima are not explicitly stated in the provided research, standard enzymatic conditions (25-37°C, pH 7.4-8.0) appear suitable based on the successful characterization studies .

• Protein Concentration: A concentration of 0.5 mg/ml has been used in solution preparations for storage and experimental use .

• Storage Conditions: For maintaining activity, storage at 4°C is recommended if using within 2-4 weeks. For longer-term storage, freezing at -20°C with the addition of a carrier protein (0.1% HSA or BSA) helps preserve activity while avoiding multiple freeze-thaw cycles .

How does C9ORF103/GNTK expression relate to cancer biology, particularly in oligodendrogliomas?

Recent research has established significant connections between C9ORF103/GNTK and cancer biology:

• Telomerase Connection: C9ORF103/GNTK expression appears to be linked to telomerase reverse transcriptase (TERT) expression in oligodendrogliomas. TERT is essential for immortality in most cancers, including oligodendrogliomas .

• Pentose Phosphate Pathway (PPP) Relationship: TERT expression in oligodendrogliomas is associated with upregulation of glucose-6-phosphate dehydrogenase (G6PD), the rate-limiting enzyme of the pentose phosphate pathway. Given that C9ORF103/GNTK contributes to the production of 6-phospho-D-gluconate, which feeds into the PPP, this suggests a metabolic relationship .

• Tumor Suppressor Potential: C9ORF103 had previously been cloned and sequenced in relation to being a plausible tumor suppressor gene associated with acute myeloid leukemia . This suggests a complex role in cancer biology that may be context-dependent.

• Metabolic Imaging Applications: Hyperpolarized δ-[1-13C]-gluconolactone has been used to image TERT expression in oligodendrogliomas, suggesting that C9ORF103/GNTK activity and gluconate metabolism could serve as metabolic biomarkers for certain cancer types .

• Experimental Approaches: Research approaches have included RNA silencing of IDNK (C9ORF103) to investigate its role in cancer contexts, particularly in relation to telomerase expression .

The emerging understanding of these relationships could potentially lead to novel diagnostic approaches or therapeutic targets in cancer treatment.

What role does C9ORF103/GNTK play in metabolic network modeling and flux analysis?

C9ORF103/GNTK holds significant importance in metabolic network modeling:

• Gap Filling in Metabolic Networks: C9ORF103 was initially identified through a metabolic network gap filling effort of Recon 1 (a comprehensive human metabolic network reconstruction) . This highlights its role in completing our understanding of human metabolic pathways.

• Erythrocyte Metabolism Modeling: Gluconate metabolism has been incorporated into the erythrocyte metabolic model iARB-RBC-283 by adding three reactions: exchange, transport, and conversion of D-gluconate to 6-phospho-D-gluconate using GNTK. This model represents the most complete representation of human red cell metabolism in SBML format .

• Flux Analysis Implications: The presence of functional human GNTK highlights a carbon flux route into the hexose monophosphate shunt with potential implications for human energy metabolism given the central role this pathway plays in fatty acid, nucleotide, and amino acid synthesis, as well as in combating oxidative stress .

• Constraint-Based Modeling: Assessment of gluconate's contribution to human central metabolism has been conducted through constraint-based modeling of the curated erythrocyte metabolic model, providing insights into alternative substrate utilization pathways .

• Metabolomics Integration: The activity of C9ORF103/GNTK helps explain the presence and metabolism of gluconate in human tissues, which is important for accurate metabolic modeling and interpretation of metabolomics data .

How can researchers use C9ORF103/GNTK studies to investigate unknown metabolic pathways in human physiology?

C9ORF103/GNTK research provides several approaches to explore unknown metabolic pathways:

• Tissue-Specific Expression Analysis: Analysis of publicly available expression and proteomic profiling datasets shows that human GNTK is differentially expressed in the thyroid and brain among other tissues. This differential expression pattern implies unknown or expanded roles beyond the proposed catabolic function in the mammalian kidney and liver .

• Metabolic Origins Investigation: While direct oxidation of glucose to generate gluconate is generally not perceived to take place in humans (where phosphorylation typically precedes oxidization), the presence of GNTK activity suggests alternative metabolic routes. Excluding dietary origins, the metabolic origins of gluconate remain unknown in humans and warrant investigation .

• Substrate Screening Methodology: Comprehensive screening of metabolite libraries against recombinant C9ORF103/GNTK can help identify unknown substrates or metabolic relationships. Previous screens have tested 25 carbohydrates/carbohydrate conjugates and 24 organic acids, but expanded screens may reveal new connections .

• Integration with Imaging Techniques: The use of hyperpolarized δ-[1-13C]-gluconolactone in imaging studies suggests potential applications for tracking GNTK-related metabolic activity in vivo, which could reveal unknown metabolic dynamics in different physiological or pathological states .

• Systems Biology Approaches: Combining experimental C9ORF103/GNTK data with constraint-based modeling can help predict and subsequently validate unknown metabolic functions or pathways, particularly in tissue-specific contexts where GNTK expression is prominent .

How should researchers interpret conflicting data regarding C9ORF103/GNTK tissue expression patterns?

When encountering conflicting data on C9ORF103/GNTK tissue expression patterns, researchers should:

• Compare Methodological Approaches:

  • Transcriptomic vs. proteomic data: mRNA levels may not directly correlate with protein expression

  • Antibody-based detection: different antibodies may have varying specificities and sensitivities

  • RNA-seq vs. microarray data: different platforms have distinct dynamic ranges and detection limits

• Consider Tissue Heterogeneity:

  • Bulk tissue vs. single-cell analysis: expression may vary among cell types within a tissue

  • The Human Protein Atlas data indicates differential expression in thyroid and brain tissues, but this should be verified against other datasets

• Evaluate Physiological Context:

  • Expression may vary based on metabolic state, disease condition, or developmental stage

  • GNTK's proposed roles in different tissues (catabolic in kidney/liver vs. potential unknown functions in thyroid/brain) may reflect context-dependent regulation

• Cross-Reference Multiple Datasets:

  • Integrate data from public resources such as the Human Protein Atlas, GTEx, and ENCODE

  • Consider orthogonal validation using multiple techniques (immunohistochemistry, RNA-seq, proteomics)

• Statistical Rigor Assessment:

  • Evaluate sample sizes, statistical methods, and significance thresholds used in different studies

  • Consider biological vs. technical replicates and sources of variation

What computational approaches can be used to predict novel functions or interactions of C9ORF103/GNTK?

Several computational approaches can yield insights into novel C9ORF103/GNTK functions:

• Structural Analysis and Molecular Docking:

  • Analyze the 18 amino acid insert unique to human GNTK compared to bacterial orthologs

  • Perform molecular docking simulations with potential substrates beyond gluconate and glucono-1,5-lactone

  • Identify structural motifs shared with other kinases to predict potential additional substrates

• Network-Based Predictions:

  • Integrate C9ORF103/GNTK into protein-protein interaction networks

  • Apply guilt-by-association principles to predict functions based on interaction partners

  • Use metabolic network context to identify potential metabolic roles beyond known pathways

• Evolutionary Analysis:

  • Comparative genomics across species to identify conserved versus divergent functions

  • Analysis of selection pressure on different protein domains to identify functionally important regions

  • Phylogenetic profiling to identify co-evolved genes that may function in the same pathway

• Text Mining and Literature-Based Discovery:

  • Natural language processing of scientific literature to extract relationships between C9ORF103/GNTK and other biological entities

  • Identify disjoint knowledge that, when connected, suggests novel hypotheses

• Multi-Omics Data Integration:

  • Correlate C9ORF103/GNTK expression with metabolomic profiles across tissues/conditions

  • Identify transcription factors regulating C9ORF103/GNTK to infer regulatory networks

  • Use constraint-based modeling with omics data integration to predict metabolic functions

How can researchers differentiate between direct and indirect effects of C9ORF103/GNTK manipulation in experimental systems?

To distinguish direct from indirect effects in C9ORF103/GNTK experiments:

• Temporal Analysis:

  • Monitor changes over time following GNTK perturbation

  • Direct effects typically occur more rapidly than downstream indirect consequences

  • Use time-course experiments with appropriate sampling intervals

• Dose-Response Relationships:

  • Establish quantitative relationships between GNTK activity levels and observed phenotypes

  • Direct effects often show proportional relationships to enzyme activity

  • Use partial knockdown or varying overexpression levels to establish dose-dependency

• Substrate and Product Measurements:

  • Directly measure gluconate and 6-phospho-D-gluconate levels

  • Monitor ATP consumption specifically attributed to GNTK activity

  • Use isotope labeling to track metabolic flux through GNTK-mediated reactions

• Rescue Experiments:

  • After GNTK knockdown or inhibition, attempt to rescue phenotypes by:

    • Adding 6-phospho-D-gluconate (the product of GNTK activity)

    • Expressing GNTK mutants with altered catalytic properties

    • Manipulating downstream pathways independently

• Cell-Free Systems:

  • Reconstitute GNTK activity in vitro with purified components

  • Test specific biochemical hypotheses in the absence of cellular complexity

  • Compare in vitro results with cellular outcomes to identify context-dependent effects

What are common challenges in expressing and purifying active C9ORF103/GNTK, and how can they be addressed?

Researchers commonly encounter these challenges when working with C9ORF103/GNTK:

• Protein Solubility Issues:

  • Challenge: Human GNTK may form inclusion bodies when overexpressed in E. coli

  • Solution: Optimize expression conditions by lowering induction temperature (16-25°C), reducing IPTG concentration, using specialized E. coli strains (like Rosetta cells that supply rare codons), or adding solubility-enhancing tags

• Activity Loss During Purification:

  • Challenge: Enzymatic activity may decrease during purification steps

  • Solution: Include stabilizing agents (glycerol at 20%, DTT at 1mM) in purification buffers, minimize purification time, and maintain consistently cold temperatures throughout the process

• Oligomerization Variability:

  • Challenge: Native gel electrophoresis shows that GNTK exists as dimers and oligomerizes further

  • Solution: Control buffer conditions (especially salt concentration and pH) to maintain consistent oligomerization state for activity assays and structural studies

• Low Expression Yield:

  • Challenge: Obtaining sufficient quantities of purified protein

  • Solution: Optimize codon usage for E. coli expression, use rich media (TB rather than LB), and consider expression vector systems with strong promoters

• Protein Stability During Storage:

  • Challenge: Activity loss during freezing/thawing or extended storage

  • Solution: Store at 4°C if using within 2-4 weeks; for longer periods, store frozen at -20°C with a carrier protein (0.1% HSA or BSA) and avoid multiple freeze-thaw cycles

How can researchers accurately measure substrate specificity of C9ORF103/GNTK when investigating potential novel substrates?

To accurately characterize C9ORF103/GNTK substrate specificity:

• Establish Robust Activity Assays:

  • Develop sensitive spectrophotometric methods to detect ATP consumption or product formation

  • Consider coupled enzyme assays that link GNTK activity to measurable colorimetric or fluorescent outputs

  • Validate assay performance with known substrates (gluconate and glucono-1,5-lactone) before testing novel candidates

• Control for Non-Enzymatic and Background Reactions:

  • Include appropriate negative controls (heat-inactivated enzyme, no-substrate controls)

  • Account for potential spontaneous ATP hydrolysis

  • Use enzyme-free controls for each potential substrate to establish baseline measurements

• Comprehensive Substrate Screening:

  • Test structurally related compounds to establish structure-activity relationships

  • Include both known substrates of related kinases and metabolically relevant compounds

  • Use metabolite libraries covering diverse chemical classes (as demonstrated in previous screening of 25 carbohydrates/carbohydrate conjugates and 24 organic acids)

• Quantitative Kinetic Analysis:

  • Determine kinetic parameters (Km, Vmax, kcat) for each potential substrate

  • Compare catalytic efficiency (kcat/Km) across substrates to establish preference hierarchy

  • Use initial velocity measurements under conditions where <10% of substrate is consumed

• Validation Through Orthogonal Methods:

  • Confirm activity with direct product detection (e.g., mass spectrometry)

  • Use isotope labeling to track phosphate transfer from ATP to the substrate

  • Consider structural studies (crystallography, molecular docking) to confirm binding mode of novel substrates

What considerations are critical when designing gene silencing or knockout experiments for C9ORF103/GNTK in different cell types?

When designing genetic manipulation experiments for C9ORF103/GNTK:

• Cell Type-Specific Expression Levels:

  • Challenge: Baseline expression of C9ORF103/GNTK varies across tissues (higher in thyroid and brain)

  • Solution: Quantify baseline expression in your cell model before intervention and select cell types with detectable expression for knockdown studies

• Silencing Efficiency Verification:

  • Challenge: Incomplete knockdown may lead to ambiguous results

  • Solution: Validate silencing at both mRNA (qRT-PCR) and protein (Western blot) levels using well-characterized primers and antibodies

  • Consider time-course analysis to determine optimal post-silencing timepoint for experiments

• Isoform Specificity:

  • Challenge: C9ORF103 has multiple isoforms with different functionalities (only isoform I has enzymatic activity)

  • Solution: Design silencing strategies (siRNA, shRNA, CRISPR) specific to isoform I or targeting shared regions depending on experimental goals

  • Verify which isoforms are expressed in your cell model before intervention

• Functional Readouts:

  • Challenge: Selecting appropriate assays to detect phenotypic consequences

  • Solution: Measure multiple endpoints including:

    • Direct: Gluconate phosphorylation activity

    • Pathway-related: Pentose phosphate pathway flux, NADPH levels

    • Phenotypic: Proliferation, oxidative stress resistance

    • Context-specific: For cancer studies, consider telomerase activity or telomere length

• Compensatory Mechanisms:

  • Challenge: Cells may upregulate alternative pathways following GNTK manipulation

  • Solution: Consider acute (siRNA) versus chronic (stable knockdown) approaches

  • Perform time-course analyses to identify potential adaptations

  • Investigate related pathways that might compensate for GNTK loss