PFKM Human

What is PFKM and what is its fundamental role in human metabolism?

Phosphofructokinase, muscle (PFKM) is a key regulatory enzyme in the glycolytic pathway that catalyzes the phosphorylation of fructose-6-phosphate to fructose-1,6-bisphosphate. This reaction represents one of the rate-limiting steps in glycolysis, making PFKM crucial for energy metabolism regulation . In humans, PFKM functions as a subunit of the tetrameric phosphofructokinase complex, with tissue-specific variations in tetramer composition. PFKM plays a particularly important role in muscle tissue, where glycolysis is essential for rapid energy production during exercise.

From a methodological perspective, researchers studying PFKM's role in metabolism should employ both enzymatic activity assays and expression analyses to comprehensively understand its function in different physiological states. The enzyme's allosteric regulation by numerous metabolites (including ATP, AMP, and citrate) makes experimental design particularly critical when investigating its activity.

What is the genomic organization of the human PFKM gene?

The human PFKM gene is located on chromosome 12q13.11 (NCBI reference sequence number NC_000012.12). The gene spans 41,151 bases between positions 48,105,253 and 48,146,404 on chromosome 12 . The coding region consists of 2,340 base pairs encoding approximately 780 amino acids .

When designing experiments targeting specific regions of PFKM, researchers should consider its exon-intron structure and the presence of regulatory elements. The following table summarizes key genomic features of human PFKM:

Researchers should be aware that alternatively spliced transcript variants have been described for PFKM, which may have tissue-specific expression patterns or functional differences .

How does PFKM differ from other phosphofructokinase isozymes in humans?

Humans possess three distinct phosphofructokinase isozymes: PFK-muscle (PFKM), PFK-liver (PFKL), and PFK-platelet (PFKP). These isozymes have different molecular weights (PFKM: 85 kDa, PFKL: 80 kDa, PFKP: 85 kDa) and are encoded by separate genes . The isozymes function as subunits of the tetrameric PFK enzyme, with the tetramer composition varying by tissue type .

When studying phosphofructokinase in human tissue samples, researchers should employ isozyme-specific antibodies or primers to distinguish between the three forms. The methodological approach should include:

• Tissue-specific expression analysis using RT-qPCR with isozyme-specific primers

• Western blotting with antibodies that can distinguish between isozymes

• Activity assays under conditions that can differentiate isozyme contributions

Understanding the tetramer composition in different tissues is essential for interpreting experimental results correctly, as functional properties may vary depending on which isozymes are present in the tetrameric complex.

What approaches are most effective for identifying functionally significant SNPs in the PFKM gene?

Identifying functionally significant single nucleotide polymorphisms (SNPs) in the PFKM gene requires a systematic approach combining computational prediction with experimental validation. A methodical workflow should include:

• Initial SNP retrieval from databases such as dbSNP

• Filtering based on validation status, conservation, minor allele frequency (MAF), and predicted functional significance

• Application of multiple prediction tools to assess potential impact

Research has identified a total of 9,694 SNPs in the PFKM gene region, of which only 85 validated SNPs with ≥10% minor allele frequency were subjected to detailed analysis . These were further classified into 11 highly prioritized, 20 moderately prioritized, and 54 poorly prioritized SNPs based on multiple computational tools .

For computational prediction, researchers should employ a combination of tools including:

• Ensembl Genome Browser for conservation analysis

• FuncPred (SNPinfo) for predicting functional effects

• RegulomeDB for identifying regulatory potential

• SIFT and PolyPhen-2 for assessing the impact of coding variants

Conservation analysis across species is particularly valuable, as SNPs in highly conserved regions are more likely to be functionally significant. The research demonstrated that comparative analysis with 91 eutherian mammals revealed evolutionarily conserved regions in human PFKM .

How does PFKM relate to cancer development and the Warburg effect?

PFKM has been identified as a potential target for cancer therapeutic studies due to its role in the Warburg effect—a phenomenon where cancer cells preferentially utilize glycolysis even in the presence of oxygen . A genome-wide association study has specifically identified PFKM as a novel marker for breast cancer in humans .

When designing studies to investigate PFKM's role in cancer, researchers should:

• Compare PFKM expression and activity between normal and cancerous tissues

• Examine the effects of PFKM knockdown or overexpression on cancer cell proliferation, migration, and metabolism

• Investigate potential correlations between PFKM SNPs and cancer susceptibility or progression

The research literature has documented associations between PFKM genetic mutations and multiple cancer types, including:

• Breast cancer

• Bladder cancer

• Non-small cell lung cancer

• Human glioma

• Human glioblastoma

• Human melanomas

Experimental approaches should include both in vitro studies using cancer cell lines and in vivo studies using animal models or patient samples. Researchers should control for confounding variables such as tissue type, metabolic state, and genetic background when studying PFKM in cancer contexts.

What methodologies are most appropriate for in silico analysis of PFKM variants?

In silico analysis of PFKM variants requires a multi-tool approach to comprehensively assess potential functional impacts. Based on established research protocols, the following methodological workflow is recommended:

• Initial SNP selection: Retrieve all known PFKM SNPs from dbSNP database and filter based on:

  • Validation status

  • Presence in evolutionary conserved regions

  • Minor allele frequency (MAF) ≥ 0.10

  • Significance of predicted functions

• Conservation analysis: Use Ensembl Genome Browser to perform comparative genomic alignment across species (e.g., the 91 eutherian mammals used in previous research)

• Functional prediction: Apply multiple complementary tools:

  • SNPinfo (FuncPred) for predicting effects on transcription factor binding sites, splice sites, miRNA binding sites, and more

  • RegulomeDB for assessing regulatory potential

  • SIFT and PolyPhen-2 for evaluating the impact of missense variants

• Prioritization: Classify variants as highly prioritized, moderately prioritized, or poorly prioritized based on the consensus of prediction tools

• Pathway analysis: Evaluate how identified functional variants might affect PFKM's role in relevant biological pathways

This multi-layered approach allows researchers to systematically narrow down the most promising PFKM variants for further experimental validation, maximizing research efficiency and resource utilization.

What are the key considerations when designing experiments to study PFKM function?

When designing experiments to study PFKM function, researchers must carefully consider several methodological aspects:

• Variable definition and control: Clearly define independent variables (e.g., PFKM expression levels, genetic variants) and dependent variables (e.g., enzymatic activity, glycolytic flux, cellular phenotypes) . Identify potential confounding variables and establish appropriate controls.

• Hypothesis formulation: Develop specific, testable hypotheses regarding PFKM function or its relationship to specific conditions . For example: "The rs12345678 SNP in PFKM reduces enzymatic activity by disrupting the ATP binding site."

• Experimental treatments: Design interventions that specifically manipulate your independent variable, such as:

  • CRISPR-Cas9 gene editing to introduce or correct specific PFKM variants

  • siRNA or shRNA for PFKM knockdown

  • Expression vectors for wild-type or mutant PFKM overexpression

• Subject assignment: Determine whether a between-subjects or within-subjects design is more appropriate . For cell culture experiments, ensure appropriate biological replicates and randomization.

• Measurement approaches: Select appropriate techniques to measure PFKM activity or function:

  • Enzymatic assays for direct measurement of PFK activity

  • Metabolic flux analysis to assess impact on glycolysis

  • Respirometry to determine effects on cellular bioenergetics

  • Protein interaction studies to examine regulatory mechanisms

Researchers should also consider technological limitations, potential artifacts, and statistical power in their experimental design.

How should human subjects research involving PFKM be designed and documented?

Human subjects research investigating PFKM requires careful planning and documentation to ensure ethical compliance and scientific validity. Researchers should:

• Determine exempt status: Assess whether the study qualifies for exemption from federal regulations, particularly for research using de-identified specimens or data .

• Complete required documentation: For non-exempt studies, prepare comprehensive documentation including:

  • Study title and clinical trial questionnaire

  • ClinicalTrials.gov identifier if applicable

  • Conditions or focus of study

  • Detailed eligibility criteria

  • Age limits and inclusion across the lifespan

  • Protection and monitoring plans

• Protocol development: Create a detailed protocol that specifies:

  • Recruitment procedures

  • Sample collection methods

  • Data analysis approaches

  • Measures to protect participant privacy and confidentiality

• Special populations consideration: When studying PFKM in the context of rare conditions like glycogen storage disease type VII (Tarui disease), develop specialized recruitment strategies and consider implementing a delayed onset study design if appropriate .

• Data sharing plan: Establish protocols for sharing de-identified data with other researchers while maintaining compliance with privacy regulations.

The PHS Human Subjects and Clinical Trials Information form provides a comprehensive framework for documenting these considerations in grant applications .

What approaches are recommended for studying PFKM in the context of Tarui disease (glycogen storage disease VII)?

Tarui disease, or glycogen storage disease type VII, results from mutations in the PFKM gene . When investigating this rare disorder, researchers should employ the following methodological approaches:

• Genetic analysis: Screen for known pathogenic mutations in PFKM and identify novel variants using:

  • Targeted sequencing of PFKM exons and splice sites

  • Whole exome or genome sequencing for comprehensive coverage

  • Bioinformatic analysis to predict pathogenicity of novel variants

• Functional validation: Assess the impact of identified mutations on:

  • PFKM protein expression and stability

  • Enzymatic activity using purified protein or cell lysates

  • Tetramer formation and subunit interactions

  • Allosteric regulation by metabolites

• Phenotypic correlation: Relate specific mutations to clinical presentations through:

  • Detailed patient phenotyping

  • Exercise tolerance testing

  • Muscle biopsy analysis for glycogen accumulation

  • Longitudinal studies of disease progression

• Model systems: Develop and utilize:

  • Patient-derived fibroblasts or myoblasts

  • CRISPR-engineered cell lines harboring Tarui disease mutations

  • Animal models (where feasible) that recapitulate disease features

• Therapeutic exploration: Investigate potential treatment approaches:

  • Alternative metabolic pathway activation

  • Chaperone therapies for missense mutations

  • Gene therapy approaches

These methodological approaches provide a comprehensive framework for advancing our understanding of how PFKM mutations lead to Tarui disease and for developing potential therapeutic interventions.

How can evolutionary conservation analysis enhance our understanding of PFKM functionality?

Evolutionary conservation analysis provides valuable insights into functionally important regions of PFKM by identifying sequences that have been preserved across species due to selective pressure. Methodologically, researchers should:

• Perform multi-species alignment: Compare human PFKM sequences with orthologous genes across diverse species. Previous research has utilized genomic alignments across 91 eutherian mammals including primates, rodents, and other mammals .

• Identify conserved domains: Map highly conserved regions to known functional domains such as:

  • Catalytic sites

  • Allosteric regulatory sites

  • Subunit interaction interfaces

  • Post-translational modification sites

• Analyze conservation patterns: Distinguish between:

  • Absolutely conserved residues (likely essential for basic function)

  • Highly conserved regions (important for specific aspects of function)

  • Variable regions (potentially involved in species-specific adaptations)

• Integrate with structural data: Map conservation data onto three-dimensional protein structures to visualize spatial patterns of conservation.

• Apply to variant interpretation: Use conservation data to prioritize variants for functional studies, as mutations in highly conserved regions are more likely to be deleterious.

This approach can be implemented using tools such as the Ensembl Genome browser, which allows researchers to visualize alignments and identify variants in conserved regions through color-coded highlighting (yellow, green, purple, pink, and red) .

What bioinformatic approaches are recommended for integrating PFKM data with other -omics datasets?

Integrating PFKM data with other -omics datasets requires sophisticated bioinformatic approaches to reveal novel insights into its biological functions and disease associations. Recommended methodological approaches include:

• Multi-omics data collection:

  • Transcriptomics: RNA-seq to measure PFKM expression levels and splicing variants

  • Proteomics: Mass spectrometry to identify PFKM protein interactions and post-translational modifications

  • Metabolomics: Targeted and untargeted approaches to measure glycolytic intermediates

  • Genomics: SNP and variant data from sequencing studies

• Data preprocessing and normalization:

  • Apply appropriate normalization methods for each data type

  • Handle missing values using imputation techniques

  • Apply quality control filters to remove low-quality measurements

• Integration analysis techniques:

  • Correlation networks: Identify associations between PFKM expression/activity and other molecular features

  • Pathway enrichment analysis: Contextualize PFKM within metabolic and signaling pathways

  • Machine learning approaches: Develop predictive models incorporating PFKM data

  • Causal inference methods: Elucidate directional relationships in regulatory networks

• Visualization strategies:

  • Interactive multi-omics visualization tools

  • Pathway visualization with overlaid expression/activity data

  • Network diagrams showing PFKM interactions

• Validation approaches:

  • Independent dataset validation

  • Experimental confirmation of key findings

  • Literature-based validation of predicted associations

Researchers should select appropriate software tools based on their specific research questions and the types of -omics data being integrated. Popular platforms include R/Bioconductor packages, specialized multi-omics integration tools, and machine learning frameworks.

What are the most promising future research directions for PFKM studies?

Based on current knowledge and research gaps, several promising directions for future PFKM research include:

• Comprehensive characterization of PFKM variants:

  • Systematic functional validation of computationally predicted significant SNPs

  • Development of high-throughput methods to assess variant effects on enzyme kinetics

  • Population-specific studies to identify ancestry-related variations in PFKM function

• PFKM in cancer metabolism:

  • Investigation of tissue-specific PFKM regulation in different cancer types

  • Exploration of PFKM as a therapeutic target for cancers exhibiting the Warburg effect

  • Development of PFKM inhibitors or modulators with anti-cancer potential

  • Identification of synthetic lethal interactions with PFKM in cancer contexts

• Systems biology approaches:

  • Integration of PFKM into comprehensive metabolic models

  • Network analysis to identify novel regulatory interactions

  • Multi-omics studies to understand PFKM in the broader context of cellular metabolism

• Translational applications:

  • Development of improved diagnostic methods for Tarui disease

  • Exploration of PFKM as a biomarker for cancer progression or treatment response

  • Investigation of personalized therapeutic approaches based on PFKM variants

• Advanced methodological development:

  • CRISPR-based screening to systematically assess PFKM regulatory elements

  • Single-cell approaches to understand PFKM heterogeneity within tissues

  • Live-cell imaging techniques to visualize PFKM dynamics in real-time