echdc3 Antibody

What is ECHDC3 and what are its primary biological functions?

ECHDC3 (Enoyl-CoA hydratase domain-containing 3) is a protein involved in multiple biological processes, particularly lipid metabolism. Research indicates its significant role in several key pathways:

• It functions in γ-linolenate biosynthesis pathways, as evidenced by RNA sequencing analysis of ECHDC3-knockdown adipocytes

• ECHDC3 appears to influence insulin sensitivity in adipose tissue, with expression levels positively correlating with the Matsuda index (a measure of insulin sensitivity)

• It may play roles in metabolic regulation, as genetic variants of ECHDC3 have been associated with lipid metabolism

• In pathological contexts, ECHDC3 has demonstrated increased expression in CD34+ progenitor cells of acute myeloid leukemia (AML) following chemotherapy, suggesting potential roles in treatment response mechanisms

The protein contains an enoyl-CoA hydratase domain, indicating enzymatic functions potentially related to fatty acid metabolism, though its precise catalytic activities require further characterization.

How is ECHDC3 expression regulated at the genetic level?

ECHDC3 expression appears to be under complex genetic regulatory control:

• Studies have identified ECHDC3 as a cis-regulated transcript (cis-eGene) in adipose tissue, with rs34844369 identified as the top cis-eSNP (expression single nucleotide polymorphism) in the AAGMEx cohort

• Genetic regulation of ECHDC3 has been observed across different ethnic populations, with concordant effects seen in both African American and European ancestry cohorts

• In Alzheimer's disease research, specific ECHDC3 genetic variants have been associated with particular disease subtypes, suggesting context-dependent regulation mechanisms

• Transcriptional profiling indicates that ECHDC3 expression varies significantly during cellular differentiation processes, particularly in adipocyte development

The identification of specific regulatory SNPs provides valuable targets for researchers investigating the genetic modulation of ECHDC3 in different disease contexts.

What are the recommended protocols for assessing ECHDC3 expression in different tissue types?

Based on established research methodologies, the following protocols are recommended for ECHDC3 expression analysis:

For adipose tissue and differentiated adipocytes:

• RNA isolation using RNAeasy kit (Qiagen) from either tissue samples or cultured adipocytes

• Reverse transcription using QuantiTect reverse transcription kit (Qiagen)

• Quantitative real-time PCR (qRT-PCR) using Power SYBR green chemistry with the following primers:

  • Forward: 5′-ACGGCATAAGGAACATCGTC-3′

  • Reverse: 5′-AAAACACAGGCCCCTCAG-3′

• Normalization to endogenous control gene 36B4/RPLP0

For central nervous system tissues and CSF:

• Cerebrospinal fluid (CSF) proteomics approaches have successfully detected ECHDC3 in neurological disease studies

• Tandem mass spectrometry (MS) methodologies are recommended for protein-level quantification

For leukemia research:

• Isolation and enrichment of CD34+ progenitor cells from bone marrow samples

• RNA-seq analysis for transcriptome-wide profiling that includes ECHDC3

• Validation of expression using qRT-PCR as described above

It is recommended to include biological triplicates for all expression analyses to ensure statistical robustness.

What are effective approaches for ECHDC3 knockdown experiments, and what controls should be included?

Effective ECHDC3 knockdown can be achieved through the following approaches:

Lentiviral shRNA method:

• Infect target cells (e.g., preadipocytes) with lentiviral particles containing ECHDC3-specific shRNA expression vectors

• Include polybrene (8 μg/mL) during transduction to improve efficiency

• Select successfully transduced cells using puromycin (2 μg/mL)

• Critical controls:

  • Scrambled shRNA lentiviral particles as negative control

  • Confirmation of knockdown efficiency via qRT-PCR

  • Multiple biological replicates (minimum of three)

siRNA alternative approach:

• Transfect cells with siRNA specifically targeting ECHDC3 mRNA

• Include non-targeting siRNA controls

• Confirm knockdown at both mRNA level (qRT-PCR) and protein level (western blot with ECHDC3 antibody)

Validation of functional consequences:

• For adipocyte studies: measure insulin-stimulated glucose uptake using radiolabeled glucose (e.g., 0.5 μCi/mL 2-deoxy-d-[3H]glucose)

• For signaling pathway analysis: assess Akt Ser473 phosphorylation levels

• For transcriptome effects: perform RNA sequencing to identify differentially expressed genes

Rigorous experimental design should include time-course analyses to account for potential compensatory mechanisms following ECHDC3 knockdown.

How does ECHDC3 expression correlate with Alzheimer's disease pathology?

ECHDC3 has demonstrated significant associations with specific Alzheimer's disease subtypes:

• ECHDC3 genetic variants have been found enriched in the blood-brain barrier dysfunction subtype of Alzheimer's disease

• This subtype is characterized by disruptions in vascular integrity and shows association with variants in IL-34 and APP genes alongside ECHDC3

• The blood-brain barrier subtype shows specific CSF proteome signatures, with ECHDC3 potentially contributing to the pathophysiological processes in this context

• Cerebrospinal fluid biomarker analysis suggests that ECHDC3 variants may influence lipid metabolism, which could affect amyloid processing or clearance mechanisms

The association between ECHDC3 and blood-brain barrier dysfunction suggests that targeting this pathway might provide therapeutic opportunities for specific subsets of Alzheimer's disease patients, particularly those with vascular components to their pathology.

What is the emerging evidence for ECHDC3's role in cancer, particularly hematological malignancies?

Recent research has highlighted several important aspects of ECHDC3 in cancer contexts:

• ECHDC3 expression is increased in CD34+ progenitor cells of acute myeloid leukemia (AML) patients following chemotherapy, suggesting potential involvement in treatment response or resistance mechanisms

• High ECHDC3 expression has been identified as a potential poor prognostic biomarker for non-APL (non-acute promyelocytic leukemia) AML

• Functional studies using RNA interference approaches have investigated ECHDC3's role in mitochondrial DNA transcriptome regulation and chemoresistance mechanisms in leukemia cells

• LASSO regression modeling has established an ECHDC3-related gene signature with prognostic significance in AML

These findings suggest ECHDC3 may represent a potential therapeutic target for overcoming chemoresistance in AML, though further mechanistic studies are needed to elucidate the precise pathways involved.

How can researchers effectively interpret conflicting data on ECHDC3 function across different disease models?

When encountering contradictory findings regarding ECHDC3, researchers should consider:

Tissue-specific expression patterns:

• ECHDC3 may have distinct functions in adipose tissue versus hematopoietic cells or neural tissues

• Expression levels and regulatory mechanisms likely differ between tissue types

Methodological differences:

• Compare sample preparation techniques (e.g., whole tissue vs. enriched cell populations)

• Evaluate antibody specificity and validation methods when comparing protein-level studies

• Consider differences between transcriptomic and proteomic methodologies

Disease context considerations:

• ECHDC3 may have opposing roles in different pathological states (e.g., metabolic disease vs. cancer)

• Genetic background differences between study populations might influence findings

• Disease stage (early vs. late) might significantly impact ECHDC3 function

Integration approach:

• Perform pathway analysis across multiple datasets to identify common mechanisms

• Consider systems biology approaches to map ECHDC3 into broader functional networks

• Validate key findings across multiple model systems and methodologies

Researchers should focus on contextualizing contradictory findings rather than dismissing them, as these might reveal important tissue-specific or condition-specific roles of ECHDC3.

What are the most promising approaches for targeting ECHDC3 in personalized medicine applications?

Several approaches show promise for translating ECHDC3 research into personalized medicine:

Genetic screening:

• Identification of ECHDC3 genetic variants in patients might help stratify disease subtypes, particularly in Alzheimer's disease and metabolic disorders

• The rs34844369 SNP and other identified regulatory variants could serve as biomarkers for treatment response prediction

Expression-based stratification:

• In AML, ECHDC3 expression levels might identify patients at higher risk of chemoresistance

• The ECHDC3-related gene signature developed through LASSO regression modeling provides a multi-gene approach for more robust patient stratification

Therapeutic targeting approaches:

• Small molecule inhibitors targeting ECHDC3's enzymatic function

• RNA-based therapeutics (siRNA, antisense oligonucleotides) for expression modulation

• Pathway-based approaches targeting downstream effectors identified through ECHDC3 knockdown studies

Monitoring applications:

• Serial assessment of ECHDC3 expression in leukemia patients might provide early indicators of developing chemoresistance

• CSF proteomics including ECHDC3 might help track Alzheimer's disease progression in specific subtypes

Integration with AI-based predictive models, similar to approaches used for antibody design (as seen with PALM-H3), could further enhance personalized medicine applications targeting ECHDC3 .

What validation methods should be applied when selecting an ECHDC3 antibody for research applications?

Rigorous validation is essential when selecting ECHDC3 antibodies:

Specificity validation:

• Western blot analysis showing a single band at the expected molecular weight (~26 kDa)

• Testing in ECHDC3 knockdown or knockout samples as negative controls

• Immunoprecipitation followed by mass spectrometry to confirm target identity

• Testing across multiple cell lines/tissues to evaluate context-dependent specificity

Application-specific validation:

• For immunohistochemistry: comparison with RNA expression data from matching tissue regions

• For flow cytometry: parallel analysis with fluorescent protein-tagged ECHDC3 constructs

• For ChIP applications: validation using known ECHDC3-interacting proteins

Cross-reactivity assessment:

• Testing against closely related family members (other ECHDC proteins)

• Evaluation in multiple species if cross-species reactivity is claimed

Lot-to-lot consistency:

• Request data on lot-specific validation from manufacturers

• Perform in-house validation of new lots against previously validated antibodies

It is recommended to utilize the combined approach of genetic manipulation (knockdown/overexpression) and antibody detection to establish definitive validation of ECHDC3 antibodies for critical research applications.

What are the optimal fixation and sample preparation methods for ECHDC3 immunodetection in different experimental contexts?

Optimal sample preparation varies by application:

For Western blotting:

• Cell lysis in RIPA buffer supplemented with protease and phosphatase inhibitors

• Sample denaturation at 95°C for 5 minutes in Laemmli buffer with DTT or β-mercaptoethanol

• Loading 10-30 μg of total protein per lane

• Transfer to PVDF membrane (preferred over nitrocellulose for ECHDC3 detection)

For immunohistochemistry/immunofluorescence:

• Paraformaldehyde fixation (4%) provides better preservation of ECHDC3 epitopes compared to methanol fixation

• Antigen retrieval using citrate buffer (pH 6.0) with heat-induced epitope retrieval methods

• Blocking with 5% normal serum from the species of secondary antibody origin

• Overnight primary antibody incubation at 4°C for optimal signal-to-noise ratio

For flow cytometry:

• Fixation with 2% paraformaldehyde

• Permeabilization with 0.1% Triton X-100 or saponin-based buffers

• Blocking with 2% BSA in PBS

• Staining for at least 30 minutes at room temperature

For immunoprecipitation:

• Gentler lysis buffers (NP-40 or Triton X-100 based) to preserve protein-protein interactions

• Pre-clearing lysates with Protein A/G beads to reduce non-specific binding

• Optimization of antibody-to-lysate ratios through titration experiments

These protocols should be further optimized for specific tissue types, particularly for adipose tissue samples where lipid content can interfere with antibody access.

How might ECHDC3 function in the intersection between metabolism and inflammation in disease states?

The emerging research suggests several potential mechanisms at the metabolism-inflammation interface:

Metabolic regulation and inflammatory signaling:

• ECHDC3's role in γ-linolenate biosynthesis may influence production of anti-inflammatory lipid mediators

• Knockdown studies suggest ECHDC3 affects insulin signaling pathways, which are intimately connected to inflammatory processes

In Alzheimer's disease:

• ECHDC3 variants are associated with the blood-brain barrier dysfunction subtype, which features altered immune regulation at the brain-periphery interface

• Co-enrichment with IL-34 variants suggests potential involvement in neuroinflammatory processes

In hematological malignancies:

• ECHDC3 upregulation in AML after chemotherapy may represent adaptation mechanisms involving metabolic reprogramming

• Gene set enrichment analyses from ECHDC3-related signatures might reveal connections to inflammatory pathways relevant to leukemia progression

Hypothesis and research directions:

• Investigate ECHDC3's potential role in regulating metabolite-sensing inflammatory pathways (e.g., NLRP3 inflammasome)

• Examine correlations between ECHDC3 expression and inflammatory cytokine profiles in disease models

• Explore ECHDC3's potential impact on mitochondrial function and resulting effects on inflammation

• Consider ECHDC3 as a mediator of metabolic adaptations during inflammatory challenges

Multi-omics approaches integrating metabolomics with transcriptomics and proteomics will be valuable for further elucidating these connections.

What computational approaches can enhance the analysis of ECHDC3 antibody-derived data in complex disease models?

Advanced computational methods can significantly improve ECHDC3 research:

Machine learning frameworks:

• LASSO regression modeling has already demonstrated utility in developing ECHDC3-related gene signatures with prognostic value in AML

• Similar approaches to those used in antibody design (like PALM-H3) could be adapted for predicting ECHDC3 interactions and functional outcomes

Network analysis approaches:

• Protein-protein interaction networks to identify ECHDC3 functional partners

• Pathway enrichment analysis to contextualize ECHDC3 within broader metabolic frameworks

• Gene regulatory network analysis to understand transcriptional control mechanisms

Integration of multi-omics data:

• Combined analysis of proteomics, transcriptomics, and metabolomics data can provide comprehensive understanding of ECHDC3 function

• Correlation of antibody-derived protein expression data with RNA-seq and metabolic profiles

• Spatial transcriptomics/proteomics integration for tissue-specific insights

AI-assisted experimental design:

• Predictive modeling for optimal ECHDC3 targeting approaches

• Design of modified antibodies with enhanced binding properties using generative models similar to those described for SARS-CoV-2 antibodies

• Virtual screening for potential ECHDC3 inhibitors based on structural predictions

These computational approaches should be implemented alongside rigorous experimental validation to maximize their translational potential in ECHDC3 research.