CKMT2 Human

How is CKMT2 expression regulated in human tissues?

CKMT2 expression demonstrates significant responsiveness to physical activity. Analysis of public datasets has consistently shown that "CKMT2 content was up-regulated by exercise training in both humans and mice" . This exercise-induced upregulation appears to be an important adaptive response that contributes to improved mitochondrial function. The regulatory mechanisms involve complex signaling pathways that sense energy demands during physical activity, triggering transcriptional activation of CKMT2. This relationship suggests that exercise may be a potential therapeutic approach to increase CKMT2 levels in conditions where its expression is compromised.

What experimental approaches are most effective for analyzing CKMT2 localization and activity?

Effective analysis of CKMT2 requires a multi-faceted experimental approach. For localization, immunohistochemistry using specific antibodies (such as HPA051880 mentioned in the Human Protein Atlas) allows visualization of CKMT2 distribution within tissues. For quantification in plasma or tissue samples, ELISA methods provide reliable measurement, with studies employing specific kits such as the "CKMT2 enzyme-linked immunosorbent assay kit (Abbexa, Cambridge, UK, abx529771)" . Functional assessment typically involves measuring mitochondrial parameters (respiration, membrane potential) after genetic manipulation of CKMT2 expression. Mass spectrometry approaches using high-resolution systems (60,000 resolution with automatic gain control set to 3×10^6) enable sensitive detection of CKMT2 protein levels and potential post-translational modifications.

How is CKMT2 content altered in type 2 diabetes and what are the metabolic consequences?

In type 2 diabetes, CKMT2 content in skeletal muscle is significantly decreased compared to healthy individuals . This reduction correlates with disrupted creatine metabolism, characterized by increased plasma creatine and decreased muscle phosphocreatine levels. The metabolic consequences include impaired glucose handling and mitochondrial dysfunction, which are hallmarks of insulin resistance. The relationship between CKMT2 reduction and metabolic dysfunction represents a potential mechanism contributing to the pathophysiology of type 2 diabetes, where compromised mitochondrial function leads to aberrant cellular energy metabolism and reduced glucose utilization.

Does CKMT2 deficiency cause insulin resistance or is it a consequence?

Research evidence suggests that alterations in CKMT2 and creatine metabolism are consequences rather than causes of insulin resistance. Studies in C57BL/6 mice fed a high-fat diet demonstrated that "neither supplementation with creatine for 2 weeks nor treatment with the creatine analog β-GPA for 1 week induced changes in glucose tolerance, suggesting that increased circulating creatine was associated with insulin resistance rather than causing it" . These findings indicate that decreased CKMT2 content observed in diabetic patients is likely a downstream effect of metabolic dysregulation rather than an initiating factor in disease development. This distinction is critical for understanding the pathophysiological sequence in type 2 diabetes.

How does genetic manipulation of CKMT2 affect mitochondrial function in metabolic disease models?

Genetic manipulation of CKMT2 produces significant effects on mitochondrial function in metabolic disease models:

• CKMT2 silencing: In C2C12 myotubes, siRNA-mediated knockdown of CKMT2 "reduced mitochondrial respiration, membrane potential, and glucose oxidation" , mimicking the mitochondrial dysfunction observed in diabetic states.

• CKMT2 overexpression: "Electroporation-mediated overexpression of CKMT2 in skeletal muscle of high-fat diet–fed male mice increased mitochondrial respiration, independent of creatine availability" . This improvement occurred without changes in insulin sensitivity, indicating that CKMT2 directly influences mitochondrial function through mechanisms separate from insulin signaling pathways.

These experimental findings suggest that while CKMT2 deficiency may not cause insulin resistance, restoring its expression could potentially ameliorate the mitochondrial dysfunction associated with metabolic disorders, representing a possible therapeutic target.

How does CKMT2 serve as a biomarker for myocardial reperfusion injury?

CKMT2 has emerged as a promising biomarker specifically for reperfusion injury following myocardial infarction. Research has identified CKMT2 "as a differentially regulated protein in plasma of mice with reperfused but not non-reperfused AMI" . This specificity to reperfusion events makes CKMT2 particularly valuable as a diagnostic tool. The mechanism likely involves release of CKMT2 from damaged mitochondria during reperfusion-induced cellular injury. Importantly, elevated plasma CKMT2 levels show significant correlation with infarct size as determined by TTC staining, providing a quantitative relationship between biomarker levels and extent of myocardial damage .

What correlations exist between plasma CKMT2 levels and cardiac functional parameters?

Analysis reveals strong correlations between plasma CKMT2 levels and critical cardiac parameters following myocardial infarction:

• Positive correlation with infarct size (INF) compared to area at risk (AAR)

• Negative correlation with ejection fraction (EF) 24 hours post-AMI

• Positive correlation with end-systolic volume (ESV)

These relationships demonstrate that higher plasma CKMT2 levels correspond with poorer cardiac outcomes, including larger infarcts and decreased systolic function. This pattern of correlation suggests that CKMT2 release into circulation proportionally reflects the severity of mitochondrial damage during reperfusion, making it a potentially valuable prognostic indicator for post-infarction cardiac recovery and remodeling.

What methodological approaches are optimal for measuring CKMT2 in plasma for cardiovascular research?

Optimal measurement of plasma CKMT2 for cardiovascular research involves a precise methodological sequence:

• Sample collection and processing:

  • Collection of blood in EDTA tubes

  • Centrifugation at 1000×g for 15 minutes to isolate plasma

  • Immediate freezing and storage at -80°C (stable for up to six months)

• Quantification via ELISA:

  • Commercial ELISA kits (e.g., CKMT2 ELISA kit from Abbexa, Cambridge, UK)

  • Strict adherence to manufacturer's protocol

  • Use of appropriate standards and controls for accurate quantification

• Validation and correlation analysis:

  • Correlation with established markers of cardiac injury

  • Statistical analysis using tools like GraphPad Prism

  • Assessment of relationships using Pearson's correlation coefficient

For discovery-phase research, mass spectrometry provides complementary untargeted analysis, using parameters such as 60,000 resolution and 3×10^6 automatic gain control for optimal sensitivity .

How does CKMT2 expression vary across different cancer types and what is its prognostic significance?

CKMT2 expression exhibits substantial heterogeneity across cancer types, with both tumor-promoting and tumor-suppressive associations depending on the specific cancer context. Research by Wang et al. indicated that "CKMT2 may be a key regulator involved in osteosarcoma formation" , suggesting a potential oncogenic role in this cancer type. Comprehensive analysis of The Cancer Genome Atlas (TCGA) data reveals tissue-specific patterns of CKMT2 expression and divergent associations with patient outcomes across different malignancies.

For prognostic assessment, researchers employ Cox proportional hazards models to calculate hazard ratios (HR), where "HR < 1 is considered to mean that CKMT2 is a protective factor for cancer; HR > 1 means that CKMT2 is a risk factor for cancer" . This statistical approach enables determination of whether high CKMT2 expression is associated with better or worse survival in each cancer type.

What bioinformatic resources and analytical approaches are most useful for studying CKMT2 in cancer genomics?

Multiple bioinformatic resources offer valuable data and tools for CKMT2 cancer research:

• Expression analysis:

  • UCSC Xena (https://xena.ucsc.edu/) provides integrated access to TCGA gene expression, clinical, and phenotypic data

  • RNA sequencing data analyzed after Log2 transformation with two-group t-tests for statistical comparison

• Genomic alterations:

  • cBioPortal (www.cbioportal.org) enables analysis of CKMT2 copy number changes and mutations

  • "Pan-cancer analysis of whole genomes (ICGC/TCGA, Nature 2020)" dataset provides comprehensive genomic information

• Protein expression:

  • The Human Protein Atlas (http://www.proteinatlas.org/) offers immunohistochemistry data on CKMT2 expression in both normal and tumor tissues

  • Specific antibody HPA051880 used for IHC analysis

• Regulatory network analysis:

  • ENCORI database (https://rnasysu.com/encori/) allows exploration of miRNA-CKMT2 interactions and construction of lncRNA-miRNA-mRNA networks

These resources collectively enable multi-dimensional analysis of CKMT2 in cancer, from gene expression patterns to regulatory mechanisms.

How can researchers design experiments to resolve contradictory findings about CKMT2's role in different cancer types?

To address contradictory findings regarding CKMT2's role in cancer, researchers should implement a comprehensive experimental design strategy:

• Multi-cancer comparison studies:

  • Analyze CKMT2 expression and function across multiple cancer types simultaneously using standardized protocols

  • Use consistent methodologies to enable direct comparison between cancer types

• Context-dependent analysis:

  • Evaluate CKMT2 function in the context of specific molecular subtypes within each cancer

  • Assess CKMT2 interactions with different signaling pathways that may be cancer-type specific

• Functional validation approaches:

  • Employ both gain-of-function (overexpression) and loss-of-function (knockdown) experiments in multiple cancer cell lines

  • Assess effects on proliferation, migration, invasion, and metabolism to capture diverse phenotypic impacts

• Integration of multi-omics data:

  • Correlate CKMT2 expression with genomic alterations, transcriptomic profiles, and proteomic patterns

  • Use computational approaches like "lncRNA-miRNA-mRNA ceRNA network" analysis to identify cancer-specific regulatory mechanisms

This systematic approach can help reconcile apparently contradictory findings by revealing context-dependent functions of CKMT2 across different cancer types and molecular backgrounds.

How can researchers effectively combine in vitro and in vivo approaches to study CKMT2 function?

An effective research strategy for CKMT2 combines complementary in vitro and in vivo approaches:

In vitro approaches:

• Cell line selection: C2C12 myotubes provide an established model for studying CKMT2 in skeletal muscle metabolism

• Genetic manipulation: siRNA for targeted CKMT2 knockdown to assess loss-of-function effects

• Functional assays: Measurement of mitochondrial respiration, membrane potential, and glucose oxidation to evaluate metabolic impact

In vivo approaches:

• Animal models: C57BL/6 mice fed high-fat diets to model metabolic disorders

• Genetic manipulation: Electroporation-mediated overexpression of CKMT2 in skeletal muscle

• Intervention studies: Creatine supplementation or treatment with creatine analogs like β-GPA

• Exercise protocols: Standardized exercise regimens to study CKMT2 upregulation

Integration strategies:

• Parallel experiments: Conduct matched experiments in cells and animals using identical interventions

• Translational validation: Confirm in vitro findings in animal models before proceeding to human studies

• Mechanistic verification: Use in vitro systems to explore molecular mechanisms identified in animal studies

This integrated approach provides robust validation across experimental systems and bridges the gap between molecular mechanisms and physiological relevance.

What statistical approaches are most appropriate for analyzing CKMT2 expression data across different experimental conditions?

Statistical analysis of CKMT2 expression requires tailored approaches depending on the experimental context:

For comparing expression levels:

• Two-group comparisons: t-tests for normally distributed data or Mann-Whitney U tests for non-parametric data

• Multiple group comparisons: One-way ANOVA followed by Tukey's post hoc test for normally distributed data or Kruskal-Wallis tests for non-parametric data

For correlation analyses:

• Pearson's correlation coefficient for linear relationships between CKMT2 levels and continuous variables

• Multiple linear regression to control for potential confounding factors

For survival analyses in cancer research:

• Kaplan-Meier survival curves with log-rank tests to compare high vs. low CKMT2 expression groups

• Cox proportional hazards models to calculate hazard ratios while adjusting for clinical covariates

For integrative analyses:

• Principal component analysis or hierarchical clustering to identify patterns across multiple variables

• Network analysis approaches for exploring relationships between CKMT2 and other genes/proteins

Statistical software tools commonly employed include GraphPad Prism (Version 10), R (Version 4.1.2 or later) with specialized packages like "survminer," "survival," and "forestplot" .

How can researchers address technical challenges in measuring CKMT2 activity versus expression?

Addressing the distinction between CKMT2 expression and activity presents several technical challenges requiring specialized approaches:

Challenges in activity measurement:

• CKMT2 exists in multiple conformational states with different enzymatic activities

• Activity can be modified by post-translational modifications independent of expression levels

• Mitochondrial membrane integrity affects functional activity

Technical solutions:

• Enzymatic activity assays:

  • Spectrophotometric coupled enzyme assays to measure ATP production

  • Radioisotope-based assays using 14C-labeled creatine to trace phosphocreatine formation

  • Maintenance of native mitochondrial environment for accurate activity assessment

• Post-translational modification analysis:

  • Phospho-specific antibodies to detect activity-modulating phosphorylation sites

  • Mass spectrometry approaches for comprehensive PTM mapping

  • Site-directed mutagenesis to create phosphomimetic or phospho-deficient variants

• Structure-function correlations:

  • Purification of native CKMT2 protein complexes to preserve functional interactions

  • Analysis of oligomeric state, which can influence enzymatic activity

  • Correlation of structural features with functional readouts

• Integrated functional assessment:

  • Simultaneous measurement of CKMT2 expression (via Western blot or ELISA) and activity

  • Calculation of activity/expression ratios to normalize for expression differences

  • Correlation with physiological endpoints like mitochondrial respiration

These approaches collectively enable researchers to distinguish between changes in CKMT2 quantity versus alterations in its enzymatic efficiency, providing deeper insight into its functional regulation in different pathophysiological contexts.