ifnlr1 Antibody

What is IFNLR1 and why is it important in research?

IFNLR1, also known as Interferon Lambda Receptor 1, is a key component of the Type III interferon receptor complex. This protein plays a crucial role in the signaling cascade triggered by Type III interferons, which are essential for antiviral defense and immune response modulation. The importance of IFNLR1 in research stems from its involvement in these fundamental biological processes, positioning it as a promising target for investigations into infectious diseases, autoimmune disorders, and viral pathogenesis. Understanding the function and regulation of IFNLR1 provides valuable insights for the development of novel therapeutics that modulate immune responses in various pathological conditions .

IFNLR1 forms a heterodimeric receptor complex with IL10RB to induce transcription of antiviral interferon-stimulated genes (ISGs). This receptor is predominantly expressed on epithelial cells, including hepatocytes, though its expression levels can be relatively low, which may limit the strength and efficacy of IFNL signaling . Research into IFNLR1 has significant implications for understanding host defense mechanisms against viral infections, particularly hepatitis viruses.

What are the different isoforms of IFNLR1 and how do they function?

There are three main IFNLR1 transcriptional variants (isoforms) that have been detected in cells, particularly in hepatocytes:

• Isoform 1: The full-length, signaling-capable form that contains complete intracellular domains necessary for signaling functions.

• Isoform 2: A variant that lacks a portion of the intracellular JAK1 binding domain, predicted to be signaling-defective.

• Isoform 3: A secreted form lacking the transmembrane domain, also predicted to be signaling-defective .

These isoforms have distinct functional properties. Isoform 1 has the highest mRNA expression in epithelial cells, including hepatocytes, and its overexpression augments IFNL-induced antiviral gene expression while also permitting de novo expression of inflammatory genes similar to type-I IFN signaling. In contrast, isoforms 2 and 3 support only a partial increase in IFNL3-induced ISG induction but do not support pro-inflammatory gene expression . This differential activity suggests that varied isoform expression could be a mechanism cells use to influence antiviral responses without causing excessive inflammation.

What applications are IFNLR1 antibodies validated for?

IFNLR1 antibodies are validated for several research applications, primarily:

These antibodies have been tested and shown to detect IFNLR1 in various sample types, including human, mouse, and rat specimens . The recommended dilutions should be optimized by end-users depending on their specific experimental conditions and sample types. Western blot applications with these antibodies typically reveal a protein band of approximately 53 kDa, while the calculated molecular weight is 58 kDa .

Detection of IFNLR1 has historically been challenging due to its low expression levels, and highly specific commercial reagents have been limited. Most detection has relied on mRNA levels rather than protein detection, but recent characterization of monoclonal antibodies targeting the protein may overcome this constraint .

How should researchers prepare samples for IFNLR1 antibody-based detection?

For optimal results when using IFNLR1 antibodies, researchers should follow these methodological steps:

• Sample preparation for Western blot:

  • Harvest cells or tissues of interest and lyse in an appropriate buffer containing protease inhibitors

  • Quantify protein concentration using standard methods (Bradford, BCA, etc.)

  • Denature samples by heating at 95°C for 5 minutes in sample buffer containing SDS and reducing agent

  • Load 20-50 μg of total protein per lane on SDS-PAGE gels

• Electrophoresis and transfer:

  • Separate proteins using 10-12% SDS-PAGE gels

  • Transfer to PVDF or nitrocellulose membranes using standard wet or semi-dry transfer systems

• Antibody incubation:

  • Block membranes using 5% non-fat milk or BSA in TBST for 1 hour at room temperature

  • Incubate with primary IFNLR1 antibody at the recommended dilution (1:1000 - 1:4000) overnight at 4°C

  • Wash thoroughly with TBST buffer

  • Incubate with appropriate HRP-conjugated secondary antibody

  • Visualize using chemiluminescence detection systems

For ELISA applications, follow manufacturer's protocols with the recommended antibody dilution of 1 μg/ml. Store antibody aliquots at -20°C and avoid repeated freeze/thaw cycles to maintain antibody integrity and performance .

How do IFNLR1 isoform expression patterns influence experimental outcomes in antiviral research?

The differential expression of IFNLR1 isoforms significantly impacts experimental outcomes in antiviral research, particularly in models of viral infection. Studies have demonstrated that manipulation of isoform expression levels can profoundly alter both the magnitude and qualitative nature of cellular responses to interferon lambda treatment.

Research using induced pluripotent stem cell-derived hepatocytes (iHeps) engineered to contain FLAG-tagged, doxycycline-inducible IFNLR1 isoform constructs has revealed several important findings:

• Isoform 1 effects:

  • Even minimal overexpression of IFNLR1 isoform 1 markedly augmented interferon-stimulated gene (ISG) expression

  • Induced de novo proinflammatory gene expression

  • Enhanced inhibition of HBV replication after IFNL treatment

  • Did not adversely affect cell viability

• Isoform 2 and 3 effects:

  • Overexpression partially augmented IFNL-induced ISG expression

  • Did not support proinflammatory gene expression

  • Had minimal impact on HBV replication

When designing experiments investigating IFNL responses, researchers should consider the endogenous isoform expression patterns in their experimental system. The relative abundance of each isoform can create significant variability in results between different cell types or between primary cells and cell lines. This might explain discrepancies in reported antiviral efficacy between different experimental systems and could have implications for interpreting clinical trial results, such as the observation that PEG-IFNL1 treatment efficacy plateaued in HBV infection studies .

What are the critical considerations for validating IFNLR1 antibody specificity in research applications?

Validating IFNLR1 antibody specificity is particularly challenging due to the existence of multiple isoforms and low expression levels in many cell types. Researchers should implement comprehensive validation strategies to ensure reliable and reproducible results:

• Positive and negative controls:

  • Use cell lines with known high IFNLR1 expression (e.g., HT-29, Jurkat, MCF7) as positive controls

  • Include IFNLR1 knockout cells or tissues as negative controls to confirm specificity

  • Compare results from multiple tissues with differential expression (e.g., lung, heart, small intestine)

• Isoform-specific validation:

  • Employ recombinant protein standards representing different isoforms

  • Use cells transfected with individual IFNLR1 isoforms as reference standards

  • Confirm detection pattern matches the expected molecular weight for each isoform (calculated MW: 58 kDa, observed MW: 53 kDa)

• Cross-reactivity assessment:

  • Test antibody against related proteins, particularly IL10RB (the heterodimerization partner)

  • Evaluate species cross-reactivity if working across human, mouse, and rat samples

  • Consider potential cross-reactivity with other cytokine receptors

• Orthogonal validation methods:

  • Compare protein detection with mRNA expression (RT-PCR)

  • Use multiple antibodies targeting different epitopes of IFNLR1

  • Employ mass spectrometry to confirm antibody-detected proteins

The challenge of IFNLR1 detection is underscored by historical reliance on mRNA levels rather than protein detection due to the limitation of highly specific commercial reagents. Recent characterization of monoclonal antibodies targeting IFNLR1 protein represents a significant advancement in the field .

How does IFNLR1 expression modulation impact experimental outcomes in HBV infection models?

Manipulation of IFNLR1 expression levels has profound effects on experimental outcomes in HBV infection models, providing valuable insights into the role of this receptor in antiviral responses. Studies using induced pluripotent stem cell-derived hepatocytes (iHeps) have revealed several key findings:

• Endogenous IFNLR1 and HBV replication:

  • Wild-type iHeps with endogenous IFNLR1 show reduced HBV DNA in supernatants and cell lysates when treated with IFNL3

  • IFNLR1-knockout iHeps do not respond to IFNL3 treatment, with no change in HBV replication

  • HBeAg levels (indicator of active HBV replication) are significantly reduced in IFNL3-treated wild-type iHeps but not in IFNLR1-knockout cells

• IFNLR1 isoform-specific effects on HBV parameters:

The data demonstrate that IFNL3 engagement with IFNLR1 isoform 1 promotes an antiviral response beyond that provided by endogenous IFNLR1 expression, and higher expression of isoform 1 further enhances this effect . These findings have important implications for experimental design when studying IFNL-mediated antiviral responses and for interpreting variable responses to IFNL therapies in clinical settings.

What methodological approaches can researchers use to study differential signaling through IFNLR1 isoforms?

Investigating differential signaling through IFNLR1 isoforms requires sophisticated methodological approaches that can distinguish between the functional outcomes of each isoform. Here are recommended methods for researchers studying this complex signaling system:

• Inducible expression systems:

  • Develop doxycycline-inducible FLAG-tagged IFNLR1 isoform constructs to control expression levels

  • Use titration of inducer to achieve different expression levels of each isoform

  • Create stable cell lines expressing individual isoforms or combinations

• Transcriptional profiling:

  • Employ RNA-seq or targeted gene expression panels to compare transcriptional responses

  • Analyze both antiviral gene sets and pro-inflammatory gene sets

  • Perform time-course experiments to capture early and late signaling events

• Protein-protein interaction studies:

  • Use co-immunoprecipitation to study IFNLR1 interactions with signaling partners

  • Analyze interactions with JAK1 and other downstream signaling molecules

  • Employ proximity ligation assays to visualize and quantify receptor-partner interactions in situ

• Signaling pathway analysis:

  • Investigate JAK-STAT pathway activation using phospho-specific antibodies

  • Monitor translocation of STAT proteins to the nucleus using immunofluorescence

  • Assess activation of alternative signaling pathways potentially engaged by different isoforms

• Functional antiviral assays:

  • Measure inhibition of viral replication using relevant viral infection models

  • Quantify viral parameters (e.g., HBV DNA, cccDNA, viral antigens)

  • Correlate antiviral effects with specific gene expression patterns

When designing these experiments, researchers should consider using IFNLR1-knockout cells as backgrounds for introducing individual isoforms to eliminate the confounding effects of endogenous receptor expression. Additionally, employing reporter systems linked to ISRE (Interferon-Stimulated Response Element) or other relevant promoters can provide quantitative readouts of signaling activity in real-time.

How should researchers optimize IFNLR1 antibody-based detection in tissues with low expression levels?

Detecting IFNLR1 in tissues with low expression presents significant technical challenges. To overcome these limitations and obtain reliable results, researchers should consider implementing the following optimization strategies:

• Sample enrichment techniques:

  • Perform subcellular fractionation to concentrate membrane proteins

  • Use immunoprecipitation to enrich for IFNLR1 before Western blot analysis

  • Consider using proximity ligation assays for in situ detection with enhanced sensitivity

• Signal amplification methods:

  • Employ tyramide signal amplification for immunohistochemistry or immunofluorescence

  • Use high-sensitivity chemiluminescent substrates for Western blot

  • Consider using biotin-streptavidin amplification systems

• Protocol optimization parameters:

• Alternative detection strategies:

  • Use targeted mass spectrometry (MRM or PRM) for sensitive protein detection

  • Consider RNA-based detection methods as surrogate indicators of protein expression

  • Employ single-cell analysis techniques to identify and study cells with higher expression levels

Researchers should be aware that IFNLR1 detection has historically relied more on mRNA levels rather than protein detection due to limitations in the specificity and sensitivity of available antibodies. Recent development of more specific monoclonal antibodies represents an important advancement that may improve detection capabilities .

What controls should be included when studying IFNLR1 isoform functionality in biological systems?

• Genetic controls:

  • IFNLR1 complete knockout cells to establish baseline in the absence of all isoforms

  • Isoform-specific knockouts or selective expression constructs

  • Wild-type cells with natural isoform expression patterns as reference points

• Expression level controls:

  • Inducible expression systems with titrated inducer concentrations to achieve physiologically relevant expression levels

  • Quantitative Western blot or flow cytometry to confirm actual protein expression levels

  • mRNA quantification to confirm transcript levels of each isoform

• Signaling pathway controls:

  • JAK inhibitors to confirm pathway specificity

  • Type I interferon receptor (IFNAR) knockout cells to exclude cross-talk effects

  • IL10RB knockout cells to confirm requirement for the heterodimeric receptor complex

• Biological response controls:

  • Positive controls using full-length IFNLR1 (isoform 1) with confirmed activity

  • Dose-response curves for IFNL ligands to establish sensitivity thresholds

  • Time-course experiments to capture both early and late signaling events

• Experimental validation controls:

  • Multiple cell lines or primary cells to ensure findings are not cell-type specific

  • Replicate experiments with different IFNL family members (IFNL1, IFNL2, IFNL3, IFNL4)

  • Alternative methods to confirm key findings (e.g., reporter assays, gene expression, protein phosphorylation)

When studying viral infection models, additional controls should include mock-infected cells, alternative antiviral stimuli (e.g., type I interferons), and measurements of multiple viral parameters to comprehensively assess antiviral effects .