PPARGC1A Monoclonal Antibody

What is PPARGC1A and why is it an important research target?

PPARGC1A (also known as PGC1α) is a transcriptional coactivator that binds to and activates various transcription factors to regulate target gene expression. It serves as a master regulator of mitochondrial oxidative phosphorylation and cellular energy metabolism . Its importance stems from its pivotal role in metabolic regulation and implication in several human diseases, most notably type 2 diabetes and cardiovascular disease (CVD) . Research targeting PPARGC1A provides insights into metabolic reprogramming and adaptive responses to dietary availability through its coordination of genes involved in glucose and fatty acid metabolism .

What are the recommended applications for PPARGC1A monoclonal antibodies?

PPARGC1A monoclonal antibodies can be used in multiple applications with specific recommended dilutions:

These antibodies have been validated for numerous additional applications as evidenced by published literature :

Note that optimal dilutions are sample-dependent and should be titrated for each specific experimental system .

What is the expected molecular weight of PPARGC1A in Western blots?

While the calculated molecular weight of PPARGC1A is 91 kDa, researchers should be aware that different isoforms and post-translational modifications result in variable observed molecular weights. Depending on the antibody and sample type:

• Some antibodies detect bands at 70 kDa and 100 kDa

• Others observe bands at 91-98 kDa

• PPARGC1A exists in various isoforms with molecular weights ranging from 30-50 kDa and 90-110 kDa

This variability is important to consider when interpreting Western blot results, as the observed band pattern may depend on tissue type, species, and specific antibody epitope.

What species reactivity should be expected with PPARGC1A antibodies?

Different PPARGC1A antibodies exhibit varying reactivity profiles:

When selecting an antibody for your research, consider both the tested and cited reactivity to ensure compatibility with your experimental model system.

How should researchers properly validate PPARGC1A antibody specificity?

Proper validation of PPARGC1A antibodies should include:

• Knockout/knockdown controls: Multiple studies referenced in the search results have utilized KD/KO approaches to validate antibody specificity . This represents the gold standard for antibody validation.

• Cross-validation with multiple antibodies: Using different antibodies targeting distinct epitopes can confirm specificity of the observed signal.

• Molecular weight verification: Given the presence of multiple isoforms, researchers should carefully compare observed band patterns with expected molecular weights.

• Positive control tissues: Skeletal muscle tissue from various species (pig, rat, mouse) has been validated as positive controls for PPARGC1A detection .

• ChIP-qPCR validation: For chromatin immunoprecipitation applications, validating enrichment at known PPARGC1A binding sites is essential, as demonstrated in published research that validated 18 out of 20 putative binding sites .

What storage and handling conditions are recommended to maintain antibody activity?

To maintain optimal activity of PPARGC1A antibodies, researchers should follow these storage and handling recommendations:

• Store at -20°C, where antibodies remain stable for one year after shipment .

• Aliquoting is unnecessary for -20°C storage.

• Antibodies are typically supplied in PBS with 0.02% sodium azide and 50% glycerol at pH 7.3 .

• Small volume (20μl) formulations may contain 0.1% BSA .

Proper storage and handling are crucial for maintaining antibody performance across multiple experiments.

What controls should be included in PPARGC1A ChIP experiments?

Based on the ChIP methodologies described in the search results, researchers should include:

• Positive control primer sets: Target genomic regions known to bind PPARGC1A or related transcription factors. For example, researchers have used primers spanning an HRE in the mouse Ntrk2 (TrkB) promoter known to be activated by HIF1A as a positive control .

• Negative control regions: Include primers targeting genomic regions not expected to bind PPARGC1A. For instance, primers spanning Exon B5 of CNS-PPARGC1A that did not contain a predicted HRE have been used .

• IgG controls: Use matched isotype IgG instead of the specific antibody to confirm the absence of non-specific binding .

• Input DNA samples: Always include input DNA as a normalization control to account for differences in chromatin preparation efficiency and DNA quantity .

• Quantitative PCR validation: Validate ChIP-seq peaks by ChIP-qPCR as demonstrated in previous research that confirmed 18 of 20 putative binding sites .

How can researchers effectively study the complex isoform diversity of PPARGC1A?

PPARGC1A exists in multiple isoforms with tissue-specific expression patterns. To effectively study this complexity:

• Transcript-specific qPCR: Design primers targeting specific exon junctions. For example, primers targeting exon 1 and exon 2 can be used for RG transcripts, while exon 5 and exon 6A can target NT-PGC-1α isoforms .

• Standard curve normalization: To directly compare amounts of different PPARGC1A transcripts, clone gene segments containing the sequences targeted by respective transcript-specific assays and use these for standard curve construction to normalize for assay efficiency .

• Isoform-specific antibodies: Select antibodies with epitopes that allow discrimination between isoform classes. Antibodies recognizing different molecular weight ranges (30-50 kDa vs. 90-110 kDa) may be useful for distinguishing between isoform groups .

• Tissue-specific considerations: The CNS-specific PPARGC1A transcripts differ from those in peripheral tissues, requiring specialized detection approaches for brain-specific studies .

What methodologies are recommended for investigating PPARGC1A's role in metabolic disease?

Based on the research methodologies referenced in the search results, investigators studying PPARGC1A in metabolic diseases should consider:

• Genetic association studies: Examine relationships between PPARGC1A genetic variants and disease phenotypes. Previous research examined nine PPARGC1A genetic variants in relation to DNA damage, type 2 diabetes, and cardiovascular disease .

• DNA damage assessment: Measure DNA damage markers such as urinary 8-hydroxydeoxyguanosine (8-OHdG) concentration as an indicator of oxidative stress related to PPARGC1A function .

• Statistical models: Use covariance analysis to examine relationships between PPARGC1A variants, DNA damage markers, and metabolic parameters. For disease outcomes, employ logistic regression models adjusted for relevant confounders (age, sex, BMI, smoking, alcohol intake, physical activity, and medication use) .

• Population admixture adjustment: When conducting genetic studies in mixed populations, use programs like STRUCTURE to adjust for population admixture that could confound genetic associations .

• Gene-environment interactions: Investigate how environmental factors, particularly physical activity which induces PPARGC1A expression, modify genetic associations with disease outcomes .

How can ChIP-seq be optimized for mapping the PPARGC1A transcriptional network?

To optimize ChIP-seq for PPARGC1A network mapping, researchers should:

• Sample preparation: For cell culture models, treatments that induce PPARGC1A activity can enhance binding detection. For example, forskolin treatment has been used to study the PPARGC1A transcriptional network in HepG2 cells .

• Peak calling and analysis: After sequencing, analyze PPARGC1A occupancy relative to both surrounding genomic regions (±2 kb from the peak) and input DNA samples to identify significant binding events .

• Conservation analysis: Evaluate the evolutionary conservation of PPARGC1A binding sites using tools like phastCons conservation scores to identify functionally important regulatory elements .

• Motif analysis: Perform de novo motif discovery on PPARGC1A-bound regions to identify DNA sequence motifs corresponding to known and novel PPARGC1A network partners .

• Integrative analysis: Combine ChIP-seq data with transcriptomic analyses to connect PPARGC1A binding events with gene expression changes and identify direct transcriptional targets .

What are the key considerations for studying PPARGC1A-mediated transcriptional regulation?

PPARGC1A operates within a complex transcriptional network. Researchers should consider:

• Co-factor interactions: PPARGC1A acts as a coactivator for multiple transcription factors. Investigating these protein-protein interactions is crucial for understanding context-specific regulation .

• Tissue-specific regulation: PPARGC1A exhibits tissue-specific activities. The regulatory network described in HepG2 cells may differ from that in skeletal muscle, adipose tissue, or neural cells .

• Integration with signaling pathways: PPARGC1A activity is regulated by post-translational modifications in response to various signaling pathways. Consider how experimental conditions might affect these modifications .

• Isoform-specific activities: Different PPARGC1A isoforms may regulate distinct sets of target genes. When studying transcriptional regulation, consider which isoforms are expressed in your experimental system .

• Chromatin context: PPARGC1A binding and activity are influenced by the surrounding chromatin environment. Consider incorporating analyses of histone modifications and chromatin accessibility .

How should researchers address variability in PPARGC1A antibody performance across applications?

When faced with inconsistent antibody performance:

• Application-specific optimization: Different applications require distinct optimization strategies. For Western blotting, consider longer blocking times and optimized antibody dilutions (1:5000-1:50000). For immunofluorescence, lower dilutions (1:1000-1:4000) are typically required .

• Sample preparation considerations: PPARGC1A detection can be affected by sample preparation. For western blotting, ensure complete protein denaturation and consider the lysis buffer composition to preserve the epitope.

• Cross-validation: When possible, validate findings using multiple antibodies targeting different epitopes of PPARGC1A, particularly when studying novel experimental systems.

• Positive controls: Include validated positive control samples such as pig, rat, or mouse skeletal muscle tissue, which consistently show strong PPARGC1A expression .

• Adjusting for isoform diversity: Consider that different antibodies may preferentially detect specific PPARGC1A isoforms. When troubleshooting, evaluate whether observed variability might reflect biological differences in isoform expression rather than technical issues.

What strategies can resolve discrepancies between expected and observed PPARGC1A molecular weights?

When observed molecular weights differ from expectations:

• Isoform awareness: PPARGC1A exists as multiple isoforms with diverse molecular weights. The full-length protein has a calculated molecular weight of 91 kDa, but observed weights can range from 70-100 kDa .

• Post-translational modifications: Consider that phosphorylation, acetylation, or other modifications can alter protein migration. PPARGC1A is highly regulated post-translationally.

• Tissue-specific expression patterns: Different tissues may express distinct isoforms. For example, CNS-specific PPARGC1A transcripts differ from those in peripheral tissues .

• Sample preparation effects: Protein degradation during sample preparation can generate fragments that may be detected by the antibody. Use fresh samples and protease inhibitors.

• Antibody epitope location: The position of the epitope recognized by the antibody affects which isoforms and fragments will be detected. Review the antibody documentation for epitope location information.

How can researchers validate the functional significance of PPARGC1A binding in ChIP experiments?

To validate the functional significance of PPARGC1A binding sites:

• Evolutionary conservation analysis: Examine the conservation of binding sites across species. Functionally important sites typically show higher conservation scores, as demonstrated in previous research .

• Integration with gene expression data: Correlate PPARGC1A binding with changes in target gene expression following PPARGC1A activation or inhibition.

• Reporter assays: Clone putative regulatory regions containing PPARGC1A binding sites into reporter constructs to assess their ability to drive transcription in response to PPARGC1A.

• Site-directed mutagenesis: Mutate specific PPARGC1A binding motifs to determine their contribution to transcriptional regulation.

• Functional genomics approaches: Use CRISPR-based approaches to delete or modify endogenous PPARGC1A binding sites and assess the impact on target gene expression and cellular phenotypes.

How is PPARGC1A being investigated in the context of DNA damage and disease mechanisms?

Current research is exploring the connection between PPARGC1A, DNA damage, and disease:

• Genetic variants and DNA damage: Studies have linked PPARGC1A genetic variants to DNA damage markers like urinary 8-hydroxydeoxyguanosine (8-OHdG), suggesting a role in oxidative stress responses .

• Disease development mechanisms: Research proposes that PPARGC1A influences the development of type 2 diabetes and cardiovascular disease through mechanisms involving DNA damage .

• Therapeutic implications: The finding that physical activity induces PPARGC1A expression suggests potential therapeutic strategies targeting this pathway to reduce disease risk .

• Mitochondrial dysfunction: Given PPARGC1A's role in mitochondrial biogenesis and function, researchers are investigating how mitochondrial dysfunction contributes to DNA damage and disease progression .

• Transcriptional regulation networks: Studies are mapping the complex PPARGC1A transcriptional networks in different tissues to understand tissue-specific disease mechanisms .

What novel methodologies are being developed for studying PPARGC1A regulatory networks?

Innovative approaches for investigating PPARGC1A networks include:

• Integrated multi-omics approaches: Combining ChIP-seq, RNA-seq, and proteomics to comprehensively map PPARGC1A-regulated networks across different physiological conditions .

• Single-cell analyses: Examining PPARGC1A function and target gene expression at the single-cell level to understand cellular heterogeneity in response to metabolic stimuli.

• CRISPR-based screens: Using CRISPR activation or interference to modulate PPARGC1A activity and identify synthetic interactions within its regulatory network.

• Tissue-specific isoform profiling: Developing methods to quantify tissue-specific PPARGC1A isoforms, such as the CNS-specific transcripts regulated by hypoxia-inducible factor (HIF1A) .

• In vivo imaging: Developing tools to visualize PPARGC1A activity in living organisms to understand its dynamic regulation in response to physiological stimuli.