PECR Human

What is PECR and what is its function in human biology?

PECR (Peroxisomal trans-2-enoyl-CoA reductase) is a metabolic enzyme involved in fatty acid metabolism. It functions in the peroxisomal fatty acid oxidation pathway, specifically in the reduction of unsaturated enoyl-CoAs. The human PECR gene maps to chromosome 2q35 . The protein contains 303 amino acids with a molecular mass of approximately 35.1 kDa when expressed recombinantly . PECR plays a critical role in the metabolism of branched-chain fatty acids and certain unsaturated fatty acids that cannot be processed by the mitochondrial β-oxidation system.

What are the structural characteristics of human PECR protein?

Human PECR is a single polypeptide chain containing 303 amino acids in its native form. When expressed recombinantly, it can be produced as a non-glycosylated protein with a His-tag fusion at the N-terminus, resulting in a total length of 327 amino acids . The molecular mass of the recombinant His-tagged protein is approximately 35.1 kDa . The protein functions within peroxisomes, where it catalyzes reduction reactions in fatty acid metabolism pathways. Its structure contains NAD(P)H-binding domains typical of reductase enzymes in this family.

How is the PECR gene regulated in human cells?

The regulation of PECR gene expression involves multiple mechanisms that respond to cellular metabolic states. While the search results don't provide specific details on PECR regulation, modern gene activation technologies like CRISPR activation systems can be used to study its expression. These systems utilize a deactivated Cas9 (dCas9) fused to activation domains like VP64, coupled with target-specific sgRNAs engineered to bind transcription activators . This synergistic activation mediator (SAM) transcription activation system can maximize the activation of endogenous PECR gene expression . Natural regulation likely involves metabolic sensing pathways that detect lipid levels and oxidative states within cells.

What experimental tools are available for studying PECR in human samples?

Several research tools are available for studying human PECR:

• PECR Lentiviral Activation Particles: These utilize a SAM transcription activation system with deactivated Cas9 (dCas9) nuclease fused to VP64 activation domain, along with sgRNA (MS2) engineered to bind MS2-P65-HSF1 fusion protein . This system allows specific upregulation of PECR gene expression.

• Recombinant PECR Protein: Human PECR recombinant protein produced in E. coli is available as a single, non-glycosylated polypeptide chain containing 327 amino acids (including a 24-amino acid His-tag at the N-terminus) . This can be used for in vitro enzyme assays or as standards in detection methods.

• PECR ELISA Kits: These kits are designed to detect native (not recombinant) PECR in appropriate sample types including undiluted body fluids and/or tissue homogenates . They provide quantitative measurement of PECR levels.

What are the optimal storage conditions for PECR research materials?

For recombinant PECR protein, the following storage recommendations apply:

Multiple freeze-thaw cycles should be avoided to maintain protein integrity . For shipping, cool pack conditions are recommended . These conditions help maintain the structural and functional integrity of the protein for research applications.

How can I design a single-case experimental design (SCED) to study PECR function in rare metabolic disorders?

When studying PECR function in rare metabolic disorders using a single-case experimental design, consider the following methodological approach:

• Baseline Phase Establishment: Collect representative baseline data to serve as a comparison for subsequent intervention phases. Ensure multiple measurements during this phase to establish stability .

• Experimental Control Implementation: Use within-subject comparison rather than between-subjects design. The participant provides their own control data, with replication of effects either within or between participants to establish experimental control .

• Randomization Component: To improve methodological rigor and internal validity, incorporate randomization elements as suggested by Kratochwill and Levin (2010) .

• Repeated Systematic Assessment: Measure dependent variables (e.g., metabolite levels, clinical symptoms) repeatedly across and within all conditions or phases of the independent variable (e.g., intervention targeting PECR function) .

• Data Analysis Approach: Consider both visual analysis and statistical approaches. Recent SCED research shows discord regarding analytic methods, so clearly justify your chosen approach .

This methodological framework is particularly valuable for studying rare conditions where large sample sizes are impractical, allowing for rigorous assessment of interventions targeting PECR function.

How can CRISPR-based technologies be applied to study PECR gene function?

CRISPR-based technologies offer powerful approaches for studying PECR gene function:

• Gene Activation (CRISPRa): PECR Lentiviral Activation Particles utilize a synergistic activation mediator (SAM) transcription activation system incorporating deactivated Cas9 (dCas9-D10A and N863A) fused to VP64 activation domain . This system works with sgRNA (MS2) engineered to bind the MS2-P65-HSF1 fusion protein, maximizing endogenous PECR gene expression . This approach enables studying the effects of PECR upregulation on cellular metabolism and function.

• Gene Knockout (CRISPRko): Although not specifically mentioned in the search results for PECR, CRISPR-Cas9 systems can be adapted for gene knockout studies as noted in references 1 and 2 of the first search result . This would involve designing guide RNAs targeting exonic regions of PECR to induce frameshift mutations.

• Precision Editing: For studying specific variants or domains, precision editing can be performed to introduce point mutations or domain modifications to assess functional consequences on enzyme activity.

• Temporal Control: Inducible CRISPR systems can provide temporal control over gene modification, allowing researchers to study PECR function at specific developmental stages or under particular metabolic conditions.

These approaches enable comprehensive functional characterization of PECR and its role in peroxisomal fatty acid metabolism.

What are the challenges in analyzing large-scale data from PECR expression studies?

Analyzing large-scale data from PECR expression studies presents several challenges that can be addressed through advanced computational approaches:

• Big Qualitative Data Handling: Large datasets containing qualitative information about PECR expression patterns across different tissues or conditions require specialized Big Qualitative (Big Qual) methods to efficiently extract meaningful insights .

• Integration of AI and Human Analysis: A combined approach of artificial intelligence methods and human expertise offers the most effective strategy. While machine learning significantly reduces analysis time, human input remains crucial for refining categories and capturing nuances in expression data .

• Natural Language Processing Applications: For analyzing literature and text-based data about PECR, NLP combined with machine learning can identify patterns and connections that might be missed in manual reviews .

• Sentiment and Thematic Analysis: When examining functional implications of PECR expression changes, combining NLP and machine learning with human input allows for nuanced interpretation of complex data relationships .

• Time and Resource Management: Machine-assisted analysis can significantly reduce processing time for large datasets while maintaining analytical depth when properly combined with expert human oversight .

This hybrid approach to data analysis enables researchers to efficiently process large PECR expression datasets while preserving the nuance and complexity necessary for meaningful biological interpretation.

How can I design experiments to investigate potential interactions between PECR and other fatty acid metabolism enzymes?

Designing experiments to investigate PECR interactions with other fatty acid metabolism enzymes requires a multi-faceted approach:

• Protein-Protein Interaction Studies:

  • Co-immunoprecipitation (Co-IP) using antibodies against native PECR to pull down potential interaction partners

  • Proximity ligation assays to visualize protein interactions in situ

  • FRET/BRET approaches using fluorescently tagged PECR and potential partners

• Functional Enzymatic Assays:

  • Design coupled enzyme assays where PECR activity is measured in the presence of varying concentrations of potential interacting enzymes

  • Monitor substrate competition or synergistic effects on reaction rates

  • Utilize recombinant PECR protein with purified partner proteins in reconstituted systems

• Genetic Modulation Approaches:

  • Employ PECR Lentiviral Activation Particles to upregulate PECR expression and observe effects on other pathway enzymes

  • Use CRISPR-based approaches to simultaneously modify PECR and potential partner genes

  • Establish genetic rescue experiments in cellular models

• Structural Biology Integration:

  • Computational docking studies using known structures

  • Hydrogen-deuterium exchange mass spectrometry to identify interaction interfaces

  • Cryo-EM of multi-enzyme complexes if stable associations are suspected

These methodological approaches provide complementary data to build a comprehensive understanding of PECR's role within the larger network of fatty acid metabolism enzymes.

What are the optimal sample preparation methods for detecting native PECR in human tissues?

For optimal detection of native PECR in human tissues, the following sample preparation methodology is recommended:

• Sample Collection and Processing:

  • Collect fresh tissue samples and process immediately or flash-freeze in liquid nitrogen

  • For ELISA detection, appropriate sample types include undiluted body fluids and/or tissue homogenates

  • Maintain cold chain during processing to prevent protein degradation

• Homogenization Protocol:

  • Homogenize tissues in buffer containing protease inhibitors

  • Use gentle mechanical disruption methods to maintain native protein conformation

  • Consider compartment-specific extraction techniques to enrich for peroxisomal proteins

• Centrifugation and Fractionation:

  • Perform differential centrifugation to separate subcellular fractions

  • For peroxisome-enriched fractions, use density gradient ultracentrifugation

  • Verify fraction purity using established peroxisomal markers

• Storage Considerations:

  • For short-term storage (up to 2-4 weeks), maintain samples at 4°C

  • For long-term storage, aliquot and freeze at -20°C

  • Consider adding carrier proteins (0.1% HSA or BSA) for extended storage

  • Avoid multiple freeze-thaw cycles

• Quality Control:

  • Assess protein integrity by western blotting prior to quantitative assays

  • Include positive and negative control samples in each experimental batch

  • Validate detection methods using recombinant PECR standards

These methodical steps ensure optimal preservation of native PECR for subsequent detection and quantification in experimental studies.

How can I validate the specificity of PECR detection methods in my experiments?

Validating the specificity of PECR detection methods requires a multi-step approach:

• Positive and Negative Controls:

  • Use recombinant human PECR protein as a positive control

  • Include samples from tissues known to have high and low PECR expression

  • Use PECR-knockout or CRISPR-modified cells as negative controls

• Antibody Validation for Immunoassays:

  • Perform western blotting to confirm single band at expected molecular weight (approximately 35.1 kDa)

  • Conduct peptide competition assays to demonstrate binding specificity

  • Use multiple antibodies targeting different epitopes for confirmation

• ELISA Method Validation:

  • When using PECR ELISA kits , perform spike-and-recovery experiments

  • Generate standard curves using recombinant protein

  • Assess cross-reactivity with structurally similar proteins

• Gene Expression Verification:

  • Correlate protein detection with mRNA levels using qRT-PCR

  • Confirm specificity of primers using melt curve analysis and sequencing

  • Use PECR Lentiviral Activation Particles as positive controls for upregulation

• Mass Spectrometry Confirmation:

  • Perform immunoprecipitation followed by mass spectrometry

  • Identify PECR-specific peptides using database searching

  • Quantify using labeled internal standards

These validation steps ensure that experimental observations genuinely reflect PECR biology rather than methodological artifacts or cross-reactivity.

What statistical approaches are most appropriate for analyzing PECR expression changes in human tissue samples?

When analyzing PECR expression changes in human tissue samples, selecting appropriate statistical approaches is critical:

• Descriptive Statistical Analysis:

  • Calculate measures of central tendency (mean, median) and dispersion (standard deviation, interquartile range)

  • Generate box plots or violin plots to visualize distribution of expression levels

  • Consider normalization to housekeeping genes or total protein for comparative analyses

• Inferential Statistics for Group Comparisons:

  • For normally distributed data: t-tests (paired or unpaired) for two groups; ANOVA for multiple groups

  • For non-normally distributed data: Mann-Whitney U or Wilcoxon signed-rank tests; Kruskal-Wallis for multiple groups

  • Include appropriate corrections for multiple comparisons (e.g., Bonferroni, Benjamini-Hochberg)

• Advanced Statistical Methods:

  • Mixed-effects models for longitudinal data or nested experimental designs

  • Regression analysis to identify factors influencing PECR expression

  • Machine learning approaches for complex datasets with multiple variables

• Single-Case Experimental Design Analysis:

  • For rare conditions or personalized medicine approaches, consider SCED statistical methods

  • Combine visual analysis with statistical techniques as noted in contemporary SCED research

  • Consider randomization tests as suggested by Kratochwill and Levin (2010)

• Data Visualization:

  • Generate heat maps for multi-tissue comparisons

  • Use volcano plots to visualize both statistical significance and magnitude of changes

  • Employ principal component analysis plots for multi-dimensional data

This comprehensive statistical approach ensures robust interpretation of PECR expression data while accounting for biological variability and experimental design considerations.

How might AI and machine learning approaches enhance PECR functional studies?

AI and machine learning approaches offer several advantages for enhancing PECR functional studies:

• Big Data Integration:

  • Machine learning can integrate multi-omics data (genomics, transcriptomics, proteomics, metabolomics) to provide comprehensive insights into PECR function

  • AI methods can identify non-obvious patterns in PECR expression across different conditions or disease states

• Natural Language Processing for Literature Mining:

  • NLP can rapidly analyze existing literature on PECR and related enzymes

  • This approach helps identify knowledge gaps and generate novel hypotheses about PECR function

• Sentiment and Thematic Analysis for Research Planning:

  • Combined NLP and machine learning with human input enables more nuanced research question development

  • These methods can identify emerging themes in PECR research from published literature

• Efficient Analysis of Large Datasets:

  • Machine-assisted analysis significantly reduces processing time while maintaining analytical depth when properly combined with expert human oversight

  • This efficiency allows researchers to analyze more comprehensive datasets than would be practical with manual methods alone

• Predictive Modeling:

  • Machine learning can predict potential PECR interactions, substrate specificities, or functional impacts of genetic variants

  • These predictions can guide targeted experimental validation, making research more efficient

The integration of human expertise with AI capabilities offers a powerful approach to efficiently analyze complex data while preserving the nuance necessary for meaningful biological interpretation of PECR function .

What are the challenges in translating PECR basic research findings to clinical applications?

Translating PECR basic research findings to clinical applications presents several challenges:

• Pathway Complexity:

  • PECR functions within complex metabolic networks, making it difficult to predict the systemic effects of modulating its activity

  • Compensatory mechanisms may mask phenotypes in experimental models

  • Comprehensive pathway analysis is required to identify optimal therapeutic targets

• Model System Limitations:

  • Single-case experimental designs (SCEDs), while valuable for rare conditions, face methodological challenges that limit widespread implementation

  • Animal models may not fully recapitulate human PECR function and regulation

  • Cell culture systems lack the complexity of whole-organism metabolism

• Biomarker Development Hurdles:

  • Identifying reliable biomarkers of PECR activity in accessible samples (blood, urine) is challenging

  • Current detection methods may require tissue samples or specialized processing

  • Standardization of assays across research and clinical laboratories is needed

• Therapeutic Development Considerations:

  • Designing specific modulators of PECR activity without off-target effects

  • Achieving appropriate drug delivery to peroxisomes

  • Addressing potential compensatory upregulation of alternative pathways

• Clinical Trial Design:

  • For rare disorders involving PECR, traditional clinical trial designs may be impractical

  • Alternative approaches like SCEDs may be more appropriate but require careful methodological consideration

  • Regulatory frameworks may need adaptation for novel trial designs

Addressing these challenges requires interdisciplinary collaboration among biochemists, clinicians, and computational biologists to bridge the gap between basic PECR research and clinical applications.

What are the most promising future directions for PECR human research?

The most promising future directions for PECR human research span multiple dimensions of science and technology:

• Integration of Advanced Technologies:

  • Combining CRISPR-based gene editing with single-cell omics approaches to understand cell-specific PECR functions

  • Applying AI and machine learning methods to integrate multi-dimensional data on PECR activity and regulation

  • Developing improved detection methods for native PECR in clinical samples

• Systems Biology Approaches:

  • Mapping PECR's place within the broader metabolic network using computational modeling

  • Identifying regulatory mechanisms and feedback loops involving PECR

  • Understanding how PECR contributes to metabolic flexibility in different tissues

• Translational Research Applications:

  • Investigating PECR's role in metabolic disorders and potential as a therapeutic target

  • Developing biomarkers based on PECR activity or metabolite profiles

  • Creating patient-derived models to study personalized responses to PECR modulation

• Methodological Innovations:

  • Refining single-case experimental designs for rare conditions affecting PECR function

  • Developing standardized protocols for PECR detection across research laboratories

  • Creating novel enzyme assays to measure PECR activity in complex biological samples