Recombinant Human Interferon lambda-1 protein (IFNL1) (Active)

What is IFNL1 and what are its alternative names?

IFNL1 belongs to the type III interferon family and is also known as IL-29 (Interleukin-29). Additional aliases include Cytokine Zcyto21, FNL1, and Interferon lambda-1. This protein is part of a novel interferon family with three members: lambda1 (IL-29), lambda2 (IL-28A), and lambda3 (IL-28B) . Unlike type I interferons with ubiquitous receptor expression, IFNL1 has a more restricted cellular target range, primarily affecting epithelial cells, particularly in the intestinal lining .

What is the molecular structure and size of recombinant human IFNL1?

Recombinant human IFNL1 is a 19.8 kDa protein containing 178 amino acid residues . The protein's structure enables it to bind to its specific receptor complex to initiate intracellular signaling cascades. While type III interferons typically feature an alpha-helical structure similar to IL-10 family cytokines, they have functional similarities to type I interferons despite structural differences.

How does IFNL1 differ functionally from type I interferons?

IFNL1 differs from type I interferons primarily in its receptor specificity and cellular targeting. While both activate similar downstream signaling pathways, IFNL1 binds to a unique class II cytokine receptor complex consisting of IFNLR1 (unique to IFN-lambdas) and IL-10R2 (shared with IL-10, IL-22, and IL-26 receptors) . The IFNLR1 receptor displays restricted expression, predominantly on epithelial cells with highest expression in intestinal epithelial cells . This restricted expression pattern results in fewer side effects when used therapeutically, compared to type I interferons whose receptors are ubiquitously expressed . Previous studies using type I interferons as IBD therapeutics demonstrated disappointing effects in both animal and clinical trials, largely due to unwanted systemic reactions .

What are the key biological functions of IFNL1?

IFNL1 exhibits several important biological functions:

• Antiviral protection: IFNL1 mediates antiviral responses by activating intracellular signaling pathways that promote expression of antiviral genes .

• Immunomodulation: IFNL1 primes dendritic cells to induce proliferation of Foxp3-expressing regulatory T cells. These dendritic cells express high levels of class I and II MHC gene products but low levels of costimulatory molecules, allowing them to specifically induce IL-2-dependent proliferation of CD4+CD25+FOXP3+ T cells with contact-dependent suppressive activity .

• Anti-inflammatory effects: In inflammatory bowel disease models, IFNL1 upregulates Foxp3 expression in T cells, reducing the production of pro-inflammatory cytokines such as IL-13 and IL-33, thereby significantly ameliorating inflammation .

• Epithelial barrier protection: IFNL1 rescues the integrity of inflamed epithelial cell monolayers, protecting epithelial barrier integrity even under inflammatory conditions .

What receptor complex mediates IFNL1 signaling?

IFNL1 signals through a class II cytokine receptor complex composed of two essential receptor proteins:

• IFNLR1 (also known as CRF2-12 or IFN-lambdaR1): This receptor component is unique to IFN-lambdas and serves as the primary binding partner for IFNL1 .

• IL-10R2 (also known as CFR2-4): This receptor component is shared with IL-10, IL-22, and IL-26 receptors .

The binding of IFNL1 to this heterodimeric receptor complex initiates intracellular signaling cascades that lead to the expression of interferon-stimulated genes (ISGs) and subsequent biological effects. The expression of IFNLR1 is largely restricted to epithelial cells, with the highest expression found in intestinal epithelial cells , which explains the tissue-specific effects of IFNL1.

How do different IFNLR1 isoforms affect IFNL1 signaling?

IFNLR1 exists in multiple isoforms that differentially affect IFNL1 signaling:

• IFNLR1 isoform 1 is the full-length, signaling-capable receptor. Overexpression of this isoform augments the magnitude of IFNL-induced interferon-stimulated gene (ISG) expression without altering the temporal kinetics. It also broadens the diversity of IFNL-induced genes by increasing formation of STAT1 homodimers, resulting in expression of IRF1, a pro-inflammatory transcription factor traditionally associated with type I but not type III interferon signaling .

• IFNLR1 isoform 2 lacks exons that encode key signaling domains, resulting in a signaling-defective protein. Expression of low levels of this isoform leads to partial induction of antiviral genes, but not pro-inflammatory genes, after IFNL3 treatment. This effect is largely abrogated at higher expression levels, suggesting a complex, concentration-dependent regulatory role .

• IFNLR1 isoform 3 is missing the transmembrane domain and is predicted to be signaling-defective .

Research suggests that these isoforms may regulate IFNL responses in a concentration-dependent manner, with non-canonical isoforms potentially serving as negative regulators of IFNL signaling .

What downstream signaling pathways are activated by IFNL1?

The downstream signaling pathways activated by IFNL1 are similar to, but not identical to, those activated by type I interferons. Upon binding to its receptor complex, IFNL1 triggers the JAK-STAT signaling pathway, leading to:

• Activation of JAK1 and TYK2 kinases associated with the receptor chains.

• Phosphorylation and activation of STAT proteins, primarily STAT1 and STAT2, which form heterodimers and associate with IRF9 to form the ISGF3 complex.

• Nuclear translocation of activated STAT complexes that bind to interferon-stimulated response elements (ISREs) in the promoters of interferon-stimulated genes.

• Induction of antiviral and immunomodulatory gene expression.

Interestingly, IFNLR1 isoform 1 overexpression can also promote formation of STAT1 homodimers, resulting in expression of pro-inflammatory genes that are not typically induced by IFNL signaling alone .

What are the differences between IFNL1, IFNL2, and IFNL3 signaling?

Although all three IFN-lambda family members signal through the same receptor complex, they exhibit distinct biological patterns:

What is the role of IFNL1 in inflammatory bowel diseases?

IFNL1 has shown promising anti-inflammatory effects in inflammatory bowel disease (IBD) models:

• Reduced pro-inflammatory cytokine production: In an in vitro IBD model using a Caco-2/Jurkat T cell coculture system, IFNL1-expressing engineered probiotics upregulated Foxp3 expression in T cells and reduced the production of pro-inflammatory cytokines such as IL-13 and IL-33, significantly ameliorating inflammation .

• Enhanced regulatory T cell population: In a 3D coculture IBD model comprising intestinal epithelial cells, myofibroblasts, and T cells, treatment with IFNL1-expressing engineered probiotics enhanced the population of regulatory T cells and increased anti-inflammatory cytokine IL-10 .

• Protection of epithelial barrier integrity: IFNL1 rescued the integrity of inflamed epithelial cell monolayers, protecting epithelial barrier function even under inflammatory conditions .

These findings suggest that IFNL1 may have therapeutic potential for IBD through multiple mechanisms, including modulating T cell responses and maintaining epithelial barrier integrity.

How has IFNL1 been engineered for therapeutic delivery?

Researchers have developed innovative approaches for IFNL1 delivery as a therapeutic agent:

• Engineered probiotic bacteria: Escherichia coli Nissle 1917 (EcN), a Gram-negative probiotic commonly used for treating gut disorders, has been engineered to produce and secrete IFNL1 in response to nitric oxide (NO), a biomarker for intestinal inflammation .

• Plasmid-based and chromosomal integration systems: Two approaches for IFNL1 expression in EcN:

  • Plasmid-based expression (EcN-IFNL1): IFNL1 production-secretion cassette carried on a plasmid

  • Chromosomal integration (EcN-gIFNL1): IFNL1 production-secretion cassette integrated into the EcN genome using lambda red recombinase-based method, targeting the nonessential lacI-lacZ region

• Secretion system: The YebF secretion tag was used to mediate secretion of IFNL1 from the engineered bacteria into the surrounding environment .

• Inducible expression: IFNL1 expression was placed under the control of a nitric oxide (NO)-inducible promoter (pNorV), allowing for targeted production in inflamed environments where NO levels are elevated .

This approach represents a potential alternative to other delivery methods such as recombinant adenovirus-mediated IFNL1 gene transfer, with advantages including stability and safety of the probiotic chassis .

What is the evidence for IFNL1's role in COVID-19 severity?

The search results provide evidence regarding IFNL1's association with COVID-19 severity:

• Reduced IFNL1 levels in severe disease: Studies have shown that reduced levels of IFNL1 and/or IFNL2, but not IFNL3, are associated with disease severity in COVID-19 .

• Statistical analysis: Research analyzed 399 Irish COVID patients stratified by WHO severity scores (WHO 1-2: n = 183, WHO 3-4: n = 145, and WHO 5-8: n = 72) and compared their IFNL1 levels with healthy donors (n = 35) .

• Genetic factors: Chi-squared analysis was performed to test for increased or decreased frequency of particular IFNL3-associated SNPs in 319 Irish COVID patients and 242 patients from a UK cohort, suggesting potential genetic influences on interferon lambda responses in COVID-19 .

• Absence of IFNL1 as a risk factor: Logistic regression models were fitted to data from patients negative for either IFNL1 (n = 138 patients) or IFNL2 (n = 105) in their serum, with WHO score as a continuous predictor, suggesting that the absence of these cytokines may be associated with disease progression .

These findings suggest that IFNL1 may play a protective role in COVID-19, with reduced levels potentially contributing to more severe disease outcomes.

What are the immunomodulatory effects of IFNL1?

IFNL1 demonstrates several important immunomodulatory effects:

These immunomodulatory properties suggest that IFNL1 may offer therapeutic benefits in inflammatory conditions through its ability to promote regulatory T cell responses and suppress excessive inflammation without the systemic side effects associated with type I interferons.

What experimental models are most appropriate for studying IFNL1 function?

The search results describe several model systems used to study IFNL1 function:

• Cell line models:

  • HEK293T cells: Used to generate stable clones with doxycycline-inducible expression of FLAG-tagged IFNLR1 isoforms, allowing precise control of receptor expression levels

  • Caco-2 cells: Intestinal epithelial cell line used in coculture systems to model intestinal inflammation

• Coculture systems:

  • Caco-2/Jurkat T cell coculture model: Used to study interactions between epithelial cells and immune cells in inflammatory conditions

  • Scaffold-based 3D coculture IBD model: Comprising intestinal epithelial cells, myofibroblasts, and T cells to better recapitulate the intestinal microenvironment

• Primary cell cultures:

  • Primary epithelial cells: Used to validate the anti-inflammatory effects of IFNL1

  • CD4+ T cells: Used to study the effects of IFNL1 on T cell differentiation and function

• Future directions:

  • Murine IBD models: Suggested for future validation of IFNL1-expressing probiotics

  • Clinical IBD samples: Proposed for translational studies to assess the relevance of findings in human disease

The choice of model system depends on the specific aspect of IFNL1 biology being investigated and the translational goals of the research.

How can IFNL1 activity be measured in experimental settings?

Several approaches have been used to measure IFNL1 activity:

• Protein quantification:

  • ELISA to measure IFNL1 concentration in supernatants or serum samples

  • Western blotting for protein detection and quantification

• Functional assays:

  • Anti-inflammatory effects in cell coculture models (e.g., Caco-2/Jurkat T cell coculture)

  • Effects on epithelial barrier integrity in cell monolayers

  • Induction of interferon-stimulated genes (ISGs) in target cells

  • Flow cytometric analysis of T cell populations (e.g., CD4+CD25+FOXP3+ regulatory T cells)

  • Measurement of cytokine production (e.g., IL-10, IL-13, IL-33)

  • Expression of tight junction proteins in epithelial cells

• Gene expression analysis:

  • qPCR for measuring expression of interferon-stimulated genes and inflammatory markers

  • Expression of iNOS gene as a marker of inflammation

The choice of assay depends on the specific aspect of IFNL1 activity being investigated, such as anti-viral effects, anti-inflammatory properties, or effects on specific cell populations.

What are the optimal methods for IFNL1 delivery in experimental systems?

The search results describe several approaches for IFNL1 delivery in experimental systems:

• Recombinant protein:

  • Direct application of purified recombinant human IFNL1 protein (19.8 kDa, 178 amino acids)

  • Used for in vitro studies to assess direct effects on target cells

• Engineered probiotics:

  • E. coli Nissle 1917 (EcN) engineered to produce and secrete IFNL1

  • Two approaches:

    • Plasmid-based expression (EcN-IFNL1): IFNL1 production-secretion cassette on a plasmid

    • Chromosomal integration (EcN-gIFNL1): IFNL1 production-secretion cassette integrated into the EcN genome

• Inducible expression systems:

  • NO-inducible promoter (pNorV) controlling IFNL1 expression in engineered probiotics

  • Doxycycline-inducible expression systems for controlled expression of IFNLR1 isoforms in stable cell lines

• Secretion systems:

  • YebF secretion tag to mediate secretion of IFNL1 from engineered bacteria

The choice of delivery method depends on the experimental goals, target tissues, and desired control over IFNL1 expression and activity.

How should researchers control for variability in IFNL1 responses across different cell types?

When studying IFNL1 responses across different cell types, researchers should implement several controls:

• Receptor expression profiling:

  • Quantify IFNLR1 and IL-10R2 expression levels across cell types

  • Determine the relative abundance of different IFNLR1 isoforms, as these significantly impact response patterns

• Dose-response relationships:

  • Establish dose-response curves for each cell type

  • Consider that IFNLR1 isoform 2 shows concentration-dependent effects, with low levels allowing partial induction of antiviral genes but higher levels abrogating this effect

• Temporal dynamics:

  • Monitor responses over time, as kinetics may vary across cell types

  • Evaluate both early and late response genes

• Genetic background:

  • Consider genetic polymorphisms associated with IFNL genes

  • Include genetic controls when comparing responses across donor-derived cells

• Validation strategies:

  • Use both protein and gene expression readouts to confirm responses

  • Compare results across multiple experimental models

  • Consider using receptor knockout or knockdown controls

These controls will help researchers to accurately interpret cell type-specific responses to IFNL1 and distinguish receptor-mediated effects from other variables.

How should contradictory data regarding IFNL1 function be interpreted?

The search results acknowledge that data regarding interferon function can be complex and often contradictory . When faced with such contradictions, researchers should consider:

• Context-dependent effects: IFNL1 functions may vary depending on:

  • Cell type (epithelial cells vs. immune cells)

  • Disease context (viral infection vs. inflammatory conditions)

  • Receptor expression levels and isoform distribution

  • Presence of other cytokines and signaling molecules

• Methodological differences:

  • In vitro vs. in vivo studies

  • Acute vs. chronic exposure

  • Concentration-dependent effects

  • Differences in measurement techniques

• Genetic factors:

  • Polymorphisms affecting IFNL function or expression

  • Variations in receptor expression or function

• Integrated analysis approaches:

  • Combine multiple experimental systems and readouts

  • Consider kinetic aspects of responses

  • Account for feedback mechanisms and compensatory pathways

For example, IFNLR1 isoform 2 has different effects on gene expression depending on its expression level, with low levels allowing partial induction of antiviral genes but higher levels abrogating this effect . Such concentration-dependent effects may explain some contradictory findings in the literature.

What statistical approaches are appropriate for analyzing IFNL1 levels in disease?

The search results describe several statistical approaches used to analyze IFNL1 levels in disease contexts:

• Comparison across groups:

  • One-way ANOVA with Tukey test to compare IFNL1 levels across different cohorts (e.g., healthy donors, HCV+ donors) or disease severity groups (e.g., WHO 1-2, WHO 3-4, WHO 5-8)

• Association analysis:

  • Chi-squared analysis to test for increased or decreased frequency of particular SNPs associated with IFNL genes in patient cohorts

  • Logistic regression models to analyze the relationship between cytokine absence and disease outcomes, with disease severity score as a continuous predictor

• Model selection:

  • Akaike Information Criterion (AIC) analysis to evaluate and compare statistical models

The appropriate statistical approach depends on:

• The specific research question

• The nature and distribution of the data

• The sample size and study design

• The potential confounding variables

Researchers should select statistical methods that account for the complexity of biological systems and the potential for interactions between multiple factors affecting IFNL1 levels and function.

How can researchers distinguish between direct and indirect effects of IFNL1?

Distinguishing between direct and indirect effects of IFNL1 requires multiple experimental approaches:

• Receptor-specific analysis:

  • Use cells expressing or lacking IFNLR1 to determine direct receptor-mediated effects

  • Compare responses in cells expressing different IFNLR1 isoforms to identify signaling-dependent effects

• Temporal profiling:

  • Perform time-course experiments to distinguish primary (early) from secondary (late) responses

  • Early gene induction (0.5-4h) is more likely to represent direct effects, while later changes may be indirect

• Pathway inhibition:

  • Use JAK-STAT pathway inhibitors to block direct IFNL1 signaling

  • Compare gene expression profiles with and without pathway inhibitors

• Conditioned media experiments:

  • Transfer media from IFNL1-treated cells to untreated cells to identify soluble mediators of indirect effects

  • Compare with direct IFNL1 treatment

• In vitro vs. in vivo comparison:

  • Compare results from simplified in vitro systems with more complex models

  • The engineered probiotic models expressing IFNL1 in IBD systems provide insights into both direct epithelial effects and indirect immunomodulatory effects

These approaches will help researchers distinguish direct IFNL1-mediated effects from downstream consequences of IFNL1 signaling, providing a more nuanced understanding of IFNL1 biology.

What are the key considerations when comparing IFNL1, IFNL2, and IFNL3 data?

When comparing data across the three type III interferon family members, researchers should consider several important factors:

• Differential expression patterns:

  • IFNL1, IFNL2, and IFNL3 levels may differ across cohorts (e.g., healthy donors, HCV+ donors) and disease states

  • Statistical analyses using one-way ANOVA with Tukey test have been used to compare these differences

• Distinct associations with disease outcomes:

  • Reduced levels of IFNL1 and/or IFNL2, but not IFNL3, were associated with disease severity in certain conditions

  • This suggests non-redundant roles for the different IFNLs

• Genetic variants:

  • Different SNPs may be associated with each IFNL gene

  • Chi-squared analysis has been used to test for associations between IFNL3-associated SNPs and disease outcomes

• Methodological considerations:

  • Ensure consistent detection methods across all three IFNLs

  • Account for potential cross-reactivity in detection assays

  • Consider temporal aspects of expression and function

• Functional redundancy vs. specificity:

  • While all three IFNLs signal through the same receptor complex, they may have subtle differences in binding affinity, signaling efficiency, or induced gene patterns

  • Additional factors like receptor isoform distribution may influence differential responses to each IFNL

By carefully considering these factors, researchers can better interpret comparative data on IFNL1, IFNL2, and IFNL3, potentially revealing unique roles for each family member in different physiological and pathological contexts.