Recombinant Human Interferon gamma protein (IFNG), (Active)

What is the molecular structure of active recombinant human IFN-gamma protein?

Recombinant human IFN-gamma exists as a non-covalently linked homodimer with subunits of approximately 16.5-25 kDa, depending on glycosylation status. The protein comprises amino acids Gln24-Gln166 with an N-terminal methionine when produced in E. coli expression systems . When analyzed by SEC-MALS, the molecular weight is approximately 34.9 kDa, confirming its dimeric structure . This homodimeric conformation is essential for biological activity as it enables proper receptor binding and subsequent signal transduction. The amino acid sequence contains multiple beta-sheets and alpha-helices that contribute to its tertiary structure, which is critical for receptor recognition and downstream signaling activity. The mature protein lacks the signal peptide present in the nascent form, making it immediately available for biological activity in experimental settings.

How does IFN-gamma signal through cellular pathways?

IFN-gamma primarily signals through the JAK-STAT pathway after interaction with its receptor IFNGR1 . The signaling mechanism proceeds through several well-characterized steps: First, IFN-gamma binds to IFNGR1, causing conformational changes that allow the intracellular domain to open and associate with downstream signaling components including JAK2, JAK1, and STAT1 . This association leads to STAT1 activation through phosphorylation, followed by STAT1 dimerization, nuclear translocation, and binding to gamma-activated sequence (GAS) elements in the promoters of IFN-gamma-regulated genes . Many of the induced genes are transcription factors such as IRF1 that can further drive regulation of a subsequent wave of transcription, creating an amplification cascade . This pathway mediates most of IFN-gamma's biological effects, including upregulation of MHC molecules, activation of macrophages, and induction of antiviral proteins.

What are the key biological functions of IFN-gamma in research models?

IFN-gamma functions as a critical cytokine in both innate and adaptive immunity with multiple research-relevant activities:

• Antimicrobial defense: IFN-gamma activates macrophages and enhances their microbicidal activity against intracellular pathogens like Toxoplasma gondii . In neuronal models, IFN-gamma stimulation enables both murine and human neurons to mount anti-parasitic defenses against T. gondii infection .

• Antigen presentation enhancement: IFN-gamma plays a crucial role in both class I and class II antigen presentation pathways. It induces replacement of standard proteasome subunits with immunoproteasome subunits, increasing the quantity, quality, and repertoire of peptides available for MHC class I loading . It also upregulates MHC class II complexes by promoting expression of key processing molecules such as cathepsins B, H, and L .

• Immunomodulation: IFN-gamma exhibits complex immunoregulatory functions, including promoting Th1 cell development and activation, enhancing NK cell activity, and facilitating the chemoattraction and activation of monocytes and macrophages . It also demonstrates anti-inflammatory properties by promoting regulatory T cell development and inhibiting Th17 cell differentiation .

• Metabolic regulation: Recent research indicates IFN-gamma plays a role in metabolic pathways, as loss of immunity-related GTPases regulated by IFN-gamma can lead to non-alcoholic fatty liver disease without obesity .

What expression systems are used for producing research-grade recombinant human IFN-gamma and how do they affect protein characteristics?

Recombinant human IFN-gamma is produced using different expression systems, each conferring specific characteristics to the final protein:

How can researchers optimize IFN-gamma activity assessment in cellular models?

To properly assess IFN-gamma activity in experimental settings:

• Antiviral activity assay: A gold-standard method involves measuring protection against viral cytopathic effects. HeLa cells infected with encephalomyocarditis virus (EMC) provide a reliable model system. Activity is typically reported as EC50 values, with effective concentrations ranging from 0.15-0.75 ng/mL for high-quality preparations .

• STAT1 phosphorylation assessment: Since IFN-gamma signals through JAK-STAT pathways, measuring STAT1 phosphorylation by western blotting or flow cytometry provides a direct readout of receptor engagement and early signaling events .

• MHC upregulation: Flow cytometric analysis of MHC class I and II upregulation on appropriate cell types (e.g., monocytes, macrophages, dendritic cells) provides a functional readout of IFN-gamma activity .

• Gene expression analysis: qPCR measurement of IFN-gamma-responsive genes such as IRF1, CXCL10, or IDO1 offers a sensitive assessment of bioactivity at the transcriptional level.

• Statistical considerations: For rigorous activity assessment, three-way ANOVA analysis should be performed when comparing multiple variables, and two-way ANOVA for simpler experimental designs . Post-hoc Bonferroni testing helps determine significance between specific experimental groups.

What are the differences between carrier-free and BSA-containing IFN-gamma preparations?

Recombinant IFN-gamma is available in both carrier-free (CF) and bovine serum albumin (BSA)-containing formulations with important experimental considerations:

The choice between carrier-free and BSA-containing preparations should be determined by the specific experimental requirements. BSA can interfere with certain analytical techniques but provides stability benefits for long-term storage and dilute working solutions. For accurate activity assessment across multiple experiments, researchers should maintain consistency in the preparation type used.

How does IFN-gamma influence proteasome function and antigen presentation pathways?

IFN-gamma exerts profound effects on the proteasome system and antigen presentation machinery:

• Immunoproteasome induction: IFN-gamma stimulation replaces constitutive proteasome catalytic subunits (β1, β2, β5) with immunoproteasome subunits (β1i/LMP2, β2i/MECL-1, β5i/LMP7) . This substitution alters the cleavage specificity of the proteasome complex.

• Proteasome activator induction: IFN-gamma upregulates the expression of PA28 (11S regulator), which associates with the proteasome and further modifies its proteolytic cleavage preferences to generate peptides more suitable for MHC loading .

• Enhanced peptide generation efficiency: The combined effects of immunoproteasome formation and PA28 association increase the quantity, quality, and diversity of peptides generated for class I MHC loading .

• MHC class II pathway enhancement: IFN-gamma promotes expression of cathepsins B, H, and L, which are critical for antigen processing in the MHC class II pathway .

The experimental implications of these effects are significant for researchers working on antigen presentation, T cell responses, and vaccine development. When designing experiments, researchers should consider the timing of IFN-gamma exposure, as immunoproteasome formation typically requires 24-48 hours to reach maximal levels.

What methodological approaches can be used to study IFN-gamma's role in stem cell biology?

IFN-gamma has emerged as an important regulator in stem cell biology with several key applications:

• Pluripotency maintenance: IFN-gamma can be used in specific protocols to maintain stem cell pluripotency, with typical concentrations ranging from 5-20 ng/mL in culture media .

• Directed differentiation: For researchers working on directed differentiation protocols, IFN-gamma (typically at 10-50 ng/mL) can be applied at specific time points to influence lineage commitment .

• Experimental design considerations:

  • Timing of IFN-gamma addition is critical, as effects may differ between early and late stages of differentiation

  • Concentration gradients should be established for each stem cell type, as sensitivity varies significantly

  • Combined application with other cytokines (e.g., TNF-α) may yield synergistic effects

  • Pre-treatment of feeder cells with IFN-gamma can modify their supportive capacity

• Assessment methods:

  • Flow cytometry for surface marker analysis

  • qPCR for lineage-specific gene expression

  • Functional assays appropriate for the target cell type

  • Epigenetic profiling to assess chromatin remodeling induced by IFN-gamma signaling

How can researchers address variability in cellular responses to IFN-gamma?

Experimental variability in IFN-gamma responses can arise from multiple sources. Researchers can employ these methodological approaches to minimize variability:

What are the considerations for using IFN-gamma in neurological research models?

IFN-gamma has important applications in neuroscience research:

• Neuronal response capabilities: Contrary to earlier beliefs, both murine and human neurons can respond to IFN-gamma stimulation and mount anti-parasitic defenses against intracellular pathogens like Toxoplasma gondii .

• Methodological approaches:

  • For in vitro neuronal cultures, typical effective concentrations range from 10-100 ng/mL

  • Pre-treatment protocols (generally 12-24 hours) before pathogen challenge yield optimal results

  • Consider blood-brain barrier penetration issues when designing in vivo experiments

  • Co-cultures of neurons with glial cells may better recapitulate the in vivo neuroinflammatory environment

• Readout selection:

  • Gene expression analysis for IFN-gamma-responsive genes

  • Immunofluorescence for activation of signaling pathways

  • Functional assays for pathogen clearance or neuronal electrophysiology

  • Analysis of neurite outgrowth and synaptogenesis when studying developmental effects

• Potential applications:

  • Neuroinflammatory disease modeling

  • Host-pathogen interactions in the CNS

  • Neurodevelopmental research

  • Neurodegeneration studies

How can the purity and activity of recombinant IFN-gamma be validated for critical research applications?

For applications requiring high confidence in protein quality:

• Purity assessment methods:

  • SDS-PAGE with silver staining should show a single band at approximately 17 kDa under reducing conditions

  • High-performance liquid chromatography (HPLC) can provide quantitative purity assessment (target: ≥95% purity)

  • Mass spectrometry can confirm exact molecular weight and sequence coverage

• Activity validation:

  • Antiviral protection assays using HeLa cells and encephalomyocarditis virus with EC50 values of 0.15-0.75 ng/mL indicating high activity

  • STAT1 phosphorylation in responsive cell lines

  • Comparative analysis with reference standards or competitor products (activity should be at least 2-fold greater than lower-quality preparations)

• Endotoxin testing:

  • LAL (Limulus Amebocyte Lysate) assay with acceptance criteria of ≤0.05 EU/μg for most research applications

  • Consider endotoxin effects when interpreting results from highly sensitive assays

• Physical characteristics:

  • SEC-MALS analysis to confirm homodimeric structure with expected molecular weight of approximately 34.9 kDa

  • Proper folding assessment through circular dichroism or other spectroscopic methods

What are common issues when working with recombinant IFN-gamma and how can they be addressed?

How should researchers design dose-response experiments with IFN-gamma?

Proper dose-response experimental design is critical for meaningful results:

• Concentration range selection:

  • For most cell culture applications: 0.1-100 ng/mL logarithmic series

  • For antiviral assays: 0.01-10 ng/mL with closer intervals near the expected EC50 (0.15-0.75 ng/mL)

  • For neuronal applications: Consider higher ranges (10-200 ng/mL) due to potentially lower receptor expression

• Time-course considerations:

  • STAT1 phosphorylation: 5-60 minutes

  • Early gene induction: 1-6 hours

  • Protein expression changes: 6-48 hours

  • Functional effects: 24-72 hours

• Statistical design requirements:

  • Minimum of three biological replicates per concentration

  • Technical triplicates for each biological replicate

  • Inclusion of appropriate positive and negative controls

  • Statistical analysis using two-way or three-way ANOVA depending on experimental complexity

• Data presentation:

  • Log-scale concentration for x-axis

  • Clear indication of error bars (standard deviation)

  • EC50/IC50 calculation with 95% confidence intervals

  • Multiple readouts when possible (e.g., signaling activity, gene expression, functional outcome)