FDX1 Human

What is the molecular structure and localization of human FDX1?

Human FDX1 is a [2Fe-2S] cluster-containing ferredoxin protein primarily localized in mitochondria. It belongs to the evolutionary conserved family of iron-sulfur proteins with a molecular mass of approximately 14 kDa and negative charge at neutral pH. FDX1 is encoded by the FDX1 gene located on chromosome 11q22 . The protein contains a conserved Fe-S binding domain that facilitates electron transfer from NADPH via ferredoxin reductase (FDXR) to various target proteins .

Methodological approach: Subcellular localization can be confirmed using immunofluorescence microscopy with FDX1-specific antibodies, while protein structure analysis typically employs X-ray crystallography or cryo-electron microscopy. Western blotting of fractionated cellular components can further validate mitochondrial localization.

How does FDX1 differ from FDX2 in humans?

Humans possess two distinct mitochondrial ferredoxins, FDX1 and FDX2, which share 43% identity and 69% similarity in their mature forms . Despite structural similarities, they exhibit highly specific substrate preferences and distinct biological functions:

Methodological approach: Functional differentiation between FDX1 and FDX2 can be experimentally determined using RNAi-mediated depletion followed by biochemical assays measuring cytochrome P450 reduction, steroid conversion, Fe/S cluster assembly, and heme A synthesis. Complementation studies in yeast models with controlled expression can further distinguish their functional specificity .

What is the primary physiological role of FDX1 in humans?

FDX1 primarily functions as an electron shuttle in the synthesis of steroid hormones. It transfers electrons from NADPH via ferredoxin reductase (FDXR) to mitochondrial cytochrome P450 enzymes, which then catalyze the conversion of cholesterol to pregnenolone, aldosterone, and cortisol . Additionally, FDX1 participates in:

• Vitamin A/D metabolism

• Bile acid synthesis

• Lipoylation of tricarboxylic acid (TCA) cycle enzymes

• Lipid homeostasis at cellular and organismal levels

Recent research has revealed that FDX1 is essential for embryonic development, as knockout of both alleles of the Fdx1 gene leads to embryonic lethality in mouse models .

Methodological approach: The physiological roles can be investigated using tissue-specific conditional knockout models, metabolic labeling with isotope tracers for steroid biosynthesis pathways, and targeted metabolomics to identify alterations in steroid hormones, bile acids, and other affected metabolites.

How does FDX1 influence lipid metabolism and homeostasis?

FDX1 plays a critical role in lipid homeostasis, with deficiency leading to significant alterations in multiple lipid classes. Research using Fdx1+/- heterozygous mice and cell culture models has demonstrated that:

• FDX1 deficiency leads to lipid droplet accumulation, potentially through the ABCA1-SREBP1/2 pathway

• Heterozygous Fdx1+/- mice are prone to developing steatohepatitis (fatty liver inflammation)

• Lipidomic analysis reveals FDX1 deficiency affects several lipid classes:

  • Cholesterol

  • Triacylglycerides

  • Acylcarnitines

  • Ceramides

  • Phospholipids

  • Lysophospholipids

Methodological approach: Researchers should employ untargeted lipidomics using liquid chromatography-mass spectrometry (LC-MS/MS), coupled with histological assessment of tissues for lipid accumulation (Oil Red O staining). Molecular mechanisms can be elucidated by analyzing protein expression of key lipid metabolism regulators (SREBP1/2, ABCA1) using Western blotting and qRT-PCR.

What is the prognostic significance of FDX1 expression in different cancer types?

FDX1 expression has been associated with prognosis across multiple cancer types. Research indicates differential prognostic implications depending on the specific cancer:

For glioma specifically, Cox regression analysis indicates FDX1 is an independent prognostic factor after adjusting for age, gender, grade, and treatment modalities .

Methodological approach: Researchers should use Kaplan-Meier survival analysis with log-rank tests to evaluate the association between FDX1 expression and patient outcomes. Cox proportional hazards regression models should be employed to adjust for confounding variables. Expression data from TCGA, GEO, and other public databases can be utilized for initial discovery, followed by validation in independent cohorts using immunohistochemistry or qRT-PCR.

How is FDX1 involved in cuproptosis and what are the implications for cancer treatment?

Recent research has identified FDX1 as a key mediator of cuproptosis, a novel form of cell death. FDX1 contributes to the accumulation of toxic lipoylated dihydrolipoamide S-acetyltransferase (DLAT), resulting in cuproptotic cell death . This involvement has several implications for cancer research:

• FDX1-mediated cuproptosis represents a potential therapeutic vulnerability in cancer cells

• Expression of FDX1 may predict sensitivity to copper-based therapies or compounds inducing cuproptosis

• In glioma, FDX1 appears to function as a regulator of cuproptosis

• The relationship between FDX1 expression and drug sensitivity suggests potential for targeted therapeutic approaches

Methodological approach: To investigate FDX1's role in cuproptosis, researchers should employ cell viability assays with copper compounds in cell lines with modulated FDX1 expression (overexpression/knockdown). Proteomics and metabolomics analyses can identify accumulation of lipoylated proteins and metabolic alterations. Drug sensitivity screening in cell lines with varying FDX1 expression can identify potential therapeutic compounds leveraging this pathway.

What are the most effective methods for studying FDX1 function in cellular models?

Several complementary approaches are recommended for investigating FDX1 function in cellular models:

• Gene modulation techniques:

  • RNAi-mediated knockdown using siRNA or shRNA

  • CRISPR-Cas9 gene editing for complete knockout or targeted mutations

  • Inducible expression systems for controlled overexpression

• Functional assays:

  • Steroid hormone synthesis assessment using LC-MS/MS

  • Measurement of cytochrome P450 enzyme activities

  • Mitochondrial function analysis (oxygen consumption, membrane potential)

  • Lipid droplet quantification using fluorescent dyes (BODIPY, Nile Red)

• Protein interaction studies:

  • Co-immunoprecipitation to identify binding partners

  • Proximity labeling (BioID, APEX) for identifying transient interactions

  • Electron transfer assays to measure redox function

Methodological approach: Combine multiple modulation techniques with functional readouts. For example, use CRISPR-Cas9 to generate FDX1-knockout cell lines, verify by Western blot, then perform metabolic profiling focused on steroid hormones and lipids, followed by rescue experiments with wild-type and mutant FDX1.

How can FDX1 expression and activity be accurately measured in clinical samples?

Accurate assessment of FDX1 in clinical samples requires a multi-modal approach:

• Expression analysis:

  • Immunohistochemistry (IHC) for protein localization and semi-quantitative assessment

  • qRT-PCR for mRNA quantification

  • Western blotting for protein level quantification

  • RNAscope for in situ mRNA detection with cellular resolution

• Activity assessment:

  • Enzyme activity assays from tissue extracts

  • Metabolite profiling of steroid hormones as functional readouts

  • Redox status assessment of interacting partners

• Correlation with clinical parameters:

  • Integration with patient data for prognostic analysis

  • Association with treatment response metrics

  • Correlation with other molecular markers

Methodological approach: For comprehensive clinical evaluation, researchers should employ at least two complementary techniques (e.g., IHC and qRT-PCR) for expression analysis. Normalization to appropriate housekeeping genes/proteins is essential, as is the inclusion of positive and negative controls. For prognostic studies, standardized scoring systems and blinded assessment are recommended to ensure reproducibility.

How do FDX1 genomic alterations affect its function and what are their clinical implications?

Genomic alterations in FDX1 can significantly impact its function and have important clinical implications:

• Types of alterations:

  • Single nucleotide variants affecting the Fe-S cluster binding region

  • Copy number variations altering expression levels

  • Promoter methylation changes affecting transcriptional regulation

  • Splice variants producing aberrant proteins

• Functional consequences:

  • Altered electron transfer efficiency

  • Modified interaction with partner proteins

  • Changed subcellular localization

  • Variations in protein stability and half-life

• Clinical implications:

  • Potential association with steroid hormone disorders

  • Altered drug responses, particularly to compounds targeting mitochondrial function

  • Prognostic significance in cancers

  • Biomarker potential for treatment stratification

Methodological approach: Researchers should employ targeted sequencing of FDX1 in patient cohorts, followed by functional characterization of identified variants using site-directed mutagenesis and biochemical assays. Computational modeling of variant effects on protein structure can guide experimental design. Patient-derived cell models carrying FDX1 variants can be used to assess phenotypic consequences and drug responses.

What is the relationship between FDX1 expression and immune cell function in the tumor microenvironment?

FDX1 has emerging connections to immune regulation and the tumor microenvironment:

• Associations with immune cells:

  • Correlation with specific immune cell infiltration patterns

  • Potential influence on T cell and B cell function

  • Relationship with immune checkpoint expression

• Impact on immunotherapy response:

  • Elevated FDX1 expression has been linked to enhanced effectiveness of PD-L1 blockade immunotherapy in melanoma

  • FDX1 shows strong connections with immune checkpoint genes in co-expression networks

• Mechanistic relationships:

  • Potential influence on metabolic reprogramming in immune cells

  • Possible role in regulating redox status of the tumor microenvironment

  • Association with tumor mutational burden (TMB) and microsatellite instability (MSI)

Methodological approach: Single-cell RNA sequencing of tumor samples with varying FDX1 expression can reveal cell type-specific effects. Co-culture experiments with tumor cells (FDX1 modulated) and immune cells can assess functional interactions. In vivo studies using syngeneic mouse models with FDX1 manipulation followed by immune profiling and immunotherapy treatment can elucidate translational relevance.

How does FDX1 interact with other mitochondrial proteins in disease states?

FDX1 functions within a complex network of mitochondrial proteins, with interactions potentially altered in disease states:

• Key interaction partners:

  • Ferredoxin reductase (FDXR) - direct electron donor

  • Cytochrome P450 enzymes - electron acceptors

  • Components of Fe-S cluster assembly machinery

  • TCA cycle enzymes requiring lipoylation

• Alterations in disease contexts:

  • Changed stoichiometry of interaction partners

  • Post-translational modifications affecting binding affinities

  • Altered subcellular localization disrupting normal interactions

  • Competition with FDX2 for shared partners

• Disease-specific considerations:

  • Cancer: metabolic reprogramming affecting electron transfer networks

  • Fatty liver disease: altered interactions in lipid metabolism pathways

  • Neurodegenerative conditions: potential role in mitochondrial dysfunction

Methodological approach: Proximity labeling techniques (BioID, APEX) in disease models can map the FDX1 interactome under pathological conditions. Differential interaction analysis comparing healthy and diseased states can identify critical changes. Blue Native PAGE and complexome profiling can assess native protein complex formation. Cross-linking mass spectrometry can determine specific interaction interfaces that may be therapeutically targeted.