TNF a Human, His

What is human TNF-alpha and what are its primary physiological roles?

Human Tumor Necrosis Factor alpha (TNF-α) is a pleiotropic inflammatory cytokine that plays critical roles in immune regulation, inflammation, and cell death pathways. TNF-α derives its name from its originally discovered ability to cause necrosis in tumor cells, though it's now recognized as a key mediator of numerous immunological processes .

The protein exists in both soluble and transmembrane forms, each with distinct signaling capabilities and biological effects. Understanding these dual forms is essential for designing effective research approaches and therapeutic interventions targeting this pathway .

How is recombinant human TNF-alpha with His-tag produced and what are its specifications?

Recombinant human TNF-alpha for research applications is typically produced using E. coli expression systems. Commercially available preparations generally consist of the Val77-Leu233 fragment of the human TNF-α protein, sometimes with an N-terminal methionine residue . When produced with a histidine tag (His-tag), the protein contains a sequence of histidine residues (typically 6×His) that facilitates purification through metal affinity chromatography.

The production process involves:

• Gene synthesis or cloning of the human TNF-α coding sequence fused with a His-tag

• Expression in a bacterial system (typically E. coli)

• Cell lysis and initial clarification

• Purification via immobilized metal affinity chromatography (IMAC)

• Additional purification steps (e.g., size exclusion chromatography)

• Quality control testing for purity, identity, and bioactivity

The final product is typically lyophilized from a filtered solution in PBS. Products may be available with or without carrier proteins such as BSA (Bovine Serum Albumin) . The carrier-free version is recommended for applications where BSA might interfere with experiments, while the standard preparation with carrier protein offers enhanced stability for general use in cell culture systems .

What are the optimal storage and handling conditions for His-tagged human TNF-alpha?

Proper storage and handling of His-tagged human TNF-alpha is crucial for maintaining its biological activity. Based on standard practices for recombinant proteins and information from search results, the following guidelines are recommended:

Storage Recommendations:

• Store lyophilized protein at -20°C to -70°C

• Use a manual defrost freezer to avoid temperature fluctuations

• Avoid repeated freeze-thaw cycles that can degrade protein structure and activity

Reconstitution Protocol:

• For standard preparations (with carrier protein):

  • Reconstitute at 0.1-1 mg/mL in sterile PBS containing at least 0.1% human or bovine serum albumin

• For carrier-free preparations:

  • Reconstitute at 0.1-1 mg/mL in sterile PBS

Working Solutions:

• Prepare single-use aliquots to avoid repeated freeze-thaw cycles

• For short-term use (1-2 weeks), store reconstituted protein at 4°C

• For longer storage, aliquot and refreeze at -20°C to -70°C

The presence of the His-tag does not significantly alter these handling recommendations, though it may provide additional stability to the N-terminal region of the protein. For applications requiring removal of the His-tag, specific enzymatic cleavage may be necessary, depending on the design of the expression construct.

What are the most effective methods for quantifying His-tagged human TNF-alpha in different experimental systems?

Several methodologies are available for quantifying His-tagged human TNF-alpha, each with specific advantages depending on the experimental context:

ELISA (Enzyme-Linked Immunosorbent Assay):

• Gold standard for TNF-α quantification in most research settings

• Available in formats specifically designed to detect His-tagged proteins

• Uses a 4-Parameter Logistic (4PL) curve fitting method for standard curves

• Typical detection ranges of 0.5-32 pg/mL for high-sensitivity kits

• Offers high specificity and well-established protocols

Western Blot:

• Allows visualization of protein integrity and potential degradation products

• Can use either anti-TNF-α or anti-His antibodies for detection

• Provides information on molecular weight and potential multimerization

His-tag Specific Detection Systems:

• Nickel-NTA or cobalt-based detection systems that specifically bind the His-tag

• Particularly useful when working with complex mixtures or when anti-TNF antibodies show cross-reactivity

Bioactivity Assays:

• Cell-based assays measuring the biological activity of TNF-α (e.g., cytotoxicity assays using L929 cells)

• ED50 values typically in the range of 25-100 pg/mL

• Provides functional rather than merely quantitative information

Table 1: Comparison of Detection Methods for His-tagged Human TNF-α

For optimal results, method selection should consider sample type, expected concentration range, and whether quantification of total protein or only bioactive protein is required .

How should experimental controls be designed when working with His-tagged human TNF-alpha?

Proper experimental control design is critical for research involving His-tagged human TNF-alpha to ensure valid and interpretable results:

Positive Controls:

• Commercially validated recombinant human TNF-α standards with known concentration and activity

• Samples known to contain high TNF-α levels (e.g., LPS-stimulated macrophage culture supernatants)

• For His-tag specific applications, other well-characterized His-tagged proteins

Negative Controls:

• Vehicle-only treatments matching the buffer composition of the TNF-α preparation

• Unstimulated cell cultures for baseline expression studies

• Non-transfected/non-transduced cells for expression studies

Specificity Controls:

• Anti-TNF-α neutralizing antibodies to confirm observed effects are TNF-specific

• Competitive binding with non-tagged TNF-α to confirm His-tag is not interfering with function

• Heat-inactivated TNF-α to confirm biological effects require the native protein conformation

Tag-Specific Controls:

• Comparison with non-His-tagged TNF-α to assess potential tag influence on activity

• His-tag only peptides to control for potential tag-specific effects

• Tag cleavage experiments when tag removal is possible

Dose-Response Evaluations:

• Multiple concentrations of TNF-α to establish dose-response relationships

• Standard curves using log dilution series for accurate quantification

• ED50 determinations (typically 25-100 pg/mL for bioactivity)

For bioactivity assays, include established TNF-α inhibitors like adalimumab or etanercept as reference standards to benchmark neutralization efficacy . When studying transmembrane TNF-α binding, controls should include assessment of both binding affinity and effector functions (ADCC, CDC) .

What are the key methodological considerations when designing neutralization assays for human TNF-alpha?

Designing effective neutralization assays for human TNF-alpha requires attention to several methodological aspects:

Cell Line Selection:

• L929 murine fibroblasts: Classic and highly sensitive model for TNF-α cytotoxicity

• WEHI 164: Alternative cytotoxicity model with different sensitivity profile

• U937 or THP-1: Human monocytic cells for evaluating effects on TNF-α target cells

• Reporter cell lines: Engineered to express luciferase or fluorescent proteins under TNF-responsive promoters

Assay Parameters:

• TNF-α concentration: Typically use 2-5× ED50 (50-500 pg/mL) for robust response

• Incubation time: Usually 18-24 hours for cytotoxicity assays

• Sensitizing agents: Often include actinomycin D or cycloheximide to enhance TNF sensitivity

• Neutralizing agent dilution series: Logarithmic dilutions for IC50 determination

Readout Methods:

• Viability dyes (MTT, XTT, WST-1) for cytotoxicity assays

• Luciferase activity for reporter-based systems

• Flow cytometry for apoptosis markers (Annexin V, propidium iodide)

• ELISA for downstream inflammatory mediators

Data Analysis:

• Calculate percent neutralization relative to positive and negative controls

• Generate dose-response curves using 4PL fitting models

• Determine IC50 values (concentration providing 50% inhibition)

• Compare relative potencies between different neutralizing agents

Validation Considerations:

• Include reference standards with known neutralizing activity (e.g., commercial anti-TNF antibodies)

• Test specificity using irrelevant cytokines and control antibodies

• Evaluate potential matrix effects from test samples

• Assess reproducibility across multiple experiments

When comparing humanized antibodies like h357 with established therapeutics like adalimumab or etanercept, standardized neutralization assays enable meaningful potency comparisons even when binding affinities might differ slightly . The assay should capture both direct neutralization of soluble TNF-α and, where relevant, effects on transmembrane TNF-α .

How does the His-tag affect the binding kinetics and biological activity of human TNF-alpha?

The presence of a histidine tag on recombinant human TNF-alpha may influence its biochemical properties and biological activities in several ways that researchers must consider:

Binding Kinetics Effects:

• The His-tag may slightly alter the association or dissociation rates with TNF receptors

• Studies comparing tagged versus untagged versions typically show minimal affinity differences if the tag is positioned away from the receptor binding domain

• When comparing different anti-TNF agents, binding kinetics (association and dissociation rates) may be more important than absolute affinity in determining biological activity

Structural Considerations:

• TNF-α functions as a homotrimer; His-tags may influence the efficiency or stability of trimerization

• N-terminal His-tags are generally less disruptive than C-terminal tags for TNF-α activity

• Flexible linker sequences between the tag and TNF-α sequence can minimize structural interference

Receptor Activation:

• Signal transduction through TNFR1 and TNFR2 may be subtly affected by the presence of His-tags

• Cellular responses in bioactivity assays may show slight variations between tagged and untagged preparations

• ED50 values typically remain in the range of 25-100 pg/mL despite the presence of the tag

Practical Research Implications:

• For most research applications, the convenience of His-tagged purification outweighs minor activity differences

• Critical experiments may warrant comparison between tagged and enzymatically de-tagged preparations

• When absolute biological fidelity is required, tag-free TNF-α may be preferable

While the His-tag provides valuable benefits for purification and detection, researchers should acknowledge its potential influence on experimental outcomes, particularly in highly sensitive applications like structural studies, binding kinetics analyses, or therapeutic development .

What factors contribute to variability in TNF-alpha measurements across different experimental platforms?

Understanding and controlling sources of variability in TNF-alpha measurements is essential for generating reliable and reproducible research data:

Analytical Method Factors:

• Different immunoassay platforms (ELISA, multiplex, flow cytometry) show systematic differences in sensitivity and specificity

• Detection antibody clones may recognize different epitopes with varying affinity

• Standard curve preparation and fitting approaches affect quantification accuracy

Sample-Related Variables:

• Sample collection methods and anticoagulant choice influence TNF-α stability

• Processing delays can lead to ex vivo production or degradation

• Storage conditions and freeze-thaw cycles impact protein integrity

• Presence of soluble TNF receptors or autoantibodies may interfere with detection

Biological Factors:

• Circadian rhythm affects baseline TNF-α levels

• Acute stressors can rapidly alter TNF-α production

• Comorbid conditions influence inflammatory status

• Medications may suppress or stimulate TNF-α production

Technical Considerations:

• Operator technique and experience

• Equipment calibration and maintenance

• Reagent lot-to-lot variability

• Laboratory environmental conditions

Table 2: Sources of Variability in TNF-α Measurements and Mitigation Strategies

To minimize variability in TNF-α measurement, researchers should implement standardized protocols, use validated reference materials, perform regular quality control, and consider the biological context of their experiments . When comparing results across studies, attention to methodological differences is essential for proper interpretation.

How can apparent contradictions in TNF-alpha response data be reconciled through temporal dynamics analysis?

Contradictory findings in TNF-alpha research can often be reconciled by understanding the complex temporal dynamics of TNF-α responses. Search result provides an instructive example of this approach:

Biphasic Response Framework:

• Initial acute phase: Characterized by increased TNF-α production

• Secondary regulatory phase: Marked by decreased TNF-α production via negative feedback mechanisms

• The temporal progression between these phases explains seemingly contradictory observations

Case Example from Spinal Manipulative Therapy (SMT) Research:
Two studies on SMT reported opposite effects on TNF-α:

• Brennan et al. observed enhanced TNF production shortly after SMT

• Teodorczyk-Injeyan et al. reported reduced TNF synthesis at a later timepoint after SMT

These contradictory findings were reconciled by recognizing they represented different points on a biphasic response curve:

• Early response: SMT triggers acute TNF-α elevation

• Later response: Regulatory T cells (Tregs) activated by initial TNF-α provide negative feedback, reducing TNF-α levels

Methodological Approaches for Temporal Analysis:

• Time-course experiments: Sample collection at multiple timepoints to capture complete response profiles

• Mechanistic measurements: Simultaneous monitoring of TNF-α and its regulatory mechanisms (e.g., Tregs, IL-10)

• Mathematical modeling: Fitting biphasic response models to temporal data

• Cell-specific analysis: Differentiating between cell populations producing or responding to TNF-α

Table 3: Characteristics of Biphasic TNF-α Response Phases

Researchers should consider that seemingly contradictory data may represent different points on a dynamic response curve rather than true scientific disagreement . Experimental design should incorporate appropriate temporal sampling to capture these complex dynamics, especially when studying interventions that might influence both pro-inflammatory and regulatory immune mechanisms.

What statistical approaches are most appropriate for analyzing TNF-alpha concentration data?

Appropriate statistical analysis of TNF-alpha data requires careful consideration of data distribution characteristics and experimental design:

Descriptive Statistics:

• Central tendency: Median often preferred over mean due to typical non-normal distribution of cytokine data

• Dispersion: Interquartile range (IQR) or 95% confidence intervals

• Data transformation: Log transformation frequently necessary to normalize cytokine distributions

Curve Fitting for Quantification:

• 4-Parameter Logistic (4PL) regression for standard curves is the recommended approach for ELISA and similar immunoassays

• The 4PL model accounts for the non-linear relationship between concentration and signal intensity

• When using logarithmic axes with zero concentration standards, consider using a very low value (e.g., 0.01) instead of zero to avoid skewing the fit

Comparative Statistics:

• For normally distributed data (after transformation if necessary):

  • Paired or unpaired t-tests for two-group comparisons

  • ANOVA with appropriate post-hoc tests for multiple group comparisons

• For non-normally distributed data:

  • Mann-Whitney U test or Wilcoxon signed-rank test for two groups

  • Kruskal-Wallis with Dunn's post-test for multiple groups

Handling Special Cases:

• Values below detection limit: Use appropriate imputation methods or specialized statistical approaches

• Outlier analysis: Identify biological outliers versus technical anomalies

• Repeated measures: Apply mixed-effects models for longitudinal data

Quality Control Parameters:

• Monitor replicate consistency (%CV typically <15% is acceptable)

• Ensure R² value of standard curves exceeds 0.98

• Verify that back-calculated standard concentrations fall within ±20% of nominal values

• Flag samples outside the standard curve range as potentially unreliable

When analyzing complex experimental designs, such as comparing TNF-α responses between different treatment groups over time, select statistical approaches that can accommodate both the biological variability inherent in cytokine responses and the specific hypotheses being tested.

How should dose-response data for anti-TNF-alpha therapeutics be analyzed and interpreted?

Dose-response analysis for anti-TNF-alpha therapeutics provides critical information about potency, efficacy, and mechanism of action:

Curve Fitting Approaches:

• Four-Parameter Logistic (4PL) regression is the gold standard for fitting sigmoidal dose-response curves

• Linear regression of log-transformed data may be appropriate for certain response ranges

• Specialized software (GraphPad Prism, R packages) facilitates proper curve fitting and parameter extraction

Key Parameters to Extract:

• IC50 (concentration providing 50% inhibition): Primary measure of potency

• Maximum inhibition: Indicates efficacy ceiling (complete vs. partial inhibition)

• Hill slope: Reflects cooperativity and mechanism of action

• Baseline response: Establishes system-specific minimum

Comparative Analysis:

• When comparing multiple anti-TNF agents (e.g., humanized antibodies vs. established therapeutics like adalimumab or etanercept), standardize testing conditions

• Calculate relative potency ratios rather than relying solely on absolute IC50 values

• Consider area under the curve (AUC) for integrated response comparisons

Mechanistic Considerations:

• Biphasic dose-response curves may indicate multiple mechanisms

• Ceiling effects below 100% inhibition suggest TNF-independent pathways

• Parallel vs. non-parallel curves between different inhibitors suggest similar or different mechanisms of action

Reporting Standards:

• Present both graphical representations and numerical parameters

• Include measures of uncertainty (95% confidence intervals)

• Provide sample sizes, replication details, and experimental conditions

• Discuss limitations and potential sources of variability

Table 4: Interpretation Guide for Anti-TNF-α Dose-Response Parameters

Even when binding affinities differ somewhat between anti-TNF-α agents, their neutralization activities may be similar, highlighting the importance of functional rather than merely binding assays for therapeutic development .

What approaches can resolve data inconsistencies when working with different TNF-alpha detection methods?

Researchers frequently encounter inconsistencies when measuring TNF-alpha using different methodologies. Resolving these discrepancies requires systematic investigation and standardization:

Common Sources of Method Discrepancies:

• Different antibody pairs recognizing distinct epitopes

• Varying matrix effects across sample types

• Detection technologies with different sensitivities and dynamic ranges

• Cross-reactivity profiles specific to each assay system

• Standard material differences between commercial kits

Bridging Study Approach:

• Parallel testing: Analyze a panel of representative samples with all methods being compared

• Correlation analysis: Determine Pearson or Spearman correlation coefficients between methods

• Bland-Altman analysis: Evaluate systematic bias and limits of agreement

• Conversion factor development: If consistent relationships exist, derive mathematical transformations between methods

Standard Reference Material Strategy:

• Use internationally recognized reference standards where available

• Create laboratory reference preparations calibrated against international standards

• Express results relative to reference material rather than absolute concentrations

• Include internal controls across all experimental batches

Method Selection Guidance:

• Select the most appropriate method based on analytical requirements rather than attempting to harmonize incompatible approaches

• For longitudinal studies, maintain methodological consistency even if newer methods become available

• When method changes are unavoidable, conduct comprehensive bridging studies

Reporting Recommendations:

• Clearly identify the specific methodology used in all publications

• Include detailed protocols or references to standardized methods

• Report both absolute values and normalized results when appropriate

• Acknowledge method-specific limitations in data interpretation

By understanding method-specific characteristics and implementing appropriate cross-validation approaches, researchers can meaningfully interpret TNF-α data across different experimental platforms and reconcile apparent inconsistencies in the literature .

How have recent discoveries about TNF-alpha and regulatory T cells changed our understanding of its physiological role?

Recent research has revealed complex interactions between TNF-alpha and regulatory T cells (Tregs) that substantially revise our understanding of TNF-α's physiological functions:

Emerging Paradigm:
The traditional view of TNF-α as simply pro-inflammatory has evolved into a more nuanced understanding where it plays dual roles in both promoting and eventually limiting inflammation. Research has revealed that TNF-α interacts with regulatory T cells to create a biphasic response pattern .

Key Interactions with Regulatory T Cells:

• Initial TNF-α production activates immune responses and inflammation

• This same TNF-α signal subsequently enhances Treg activation and function

• Activated Tregs then provide negative feedback to reduce TNF-α production

• This creates a self-limiting inflammatory response in healthy individuals

Implications for Disease Understanding:
This bidirectional relationship helps explain several previously puzzling observations:

• Why some inflammatory conditions worsen initially with anti-TNF therapy

• How TNF can both promote inflammation and contribute to its resolution

• Why TNF-α levels may show biphasic patterns in response to various stimuli

The search results specifically highlight how this model resolved apparent contradictions in spinal manipulative therapy research, where some studies showed increased TNF-α following treatment while others showed decreased levels. By recognizing these measurements represented different timepoints on the biphasic response curve, the findings could be reconciled .

This evolving understanding suggests that therapeutic approaches targeting TNF-α might be optimized by considering timing, dose, and the specific disease context to avoid disrupting physiological regulatory mechanisms.

What are the current challenges and advances in humanizing murine anti-TNF-alpha antibodies?

The humanization of murine anti-TNF-alpha antibodies represents a critical step in developing therapeutic antibodies with reduced immunogenicity. Search result outlines key challenges and methodological advances in this area:

Humanization Approaches:

• Variable domain resurfacing: The approach used with m357 antibody, replacing non-conserved surface residues in framework regions with human counterparts while preserving antigen-binding regions

• CDR grafting: Transplanting complementarity-determining regions (CDRs) from murine antibodies onto human framework regions

• Phage display technologies: Selecting fully human antibodies against TNF-α

• Transgenic mice: Generating human antibodies in mice engineered with human immunoglobulin genes

Critical Success Factors:

• Structural modeling: Computer-assisted homology modeling to predict structure and identify critical residues

• Database utilization: Using resources like IMGT to identify the most homologous human sequences

• Solvent accessibility analysis: Computational tools like AREAIMOL to calculate solvent-accessible residues

• Functional preservation: Maintaining high antigen binding affinity and bioactivity after humanization

Quality Assessment Parameters:

• Binding affinity: Humanized h357 IgG maintained nanomolar KD (16.8 nM vs. 12.0 nM for murine version)

• Neutralization capacity: Similar bioactivity in TNF-α neutralization assays

• Effector functions: Ability to mediate ADCC and CDC upon binding to transmembrane TNF-α

• In vivo efficacy: Demonstrated inhibition in disease models like collagen antibody-induced arthritis

Ongoing Challenges:

• Balancing reduced immunogenicity against maintained binding affinity

• Preserving important effector functions like ADCC and CDC

• Optimizing binding kinetics (association/dissociation rates) beyond simple affinity measures

• Addressing epitope-specific effects when targeting different regions of TNF-α

The successful humanization of antibodies like h357 demonstrates that careful engineering can produce therapeutically promising antibodies that maintain the beneficial properties of the original murine versions while reducing immunogenicity for human applications .

How might advances in TNF-alpha research impact future therapeutic approaches?

Emerging research on TNF-alpha is driving innovations that may reshape therapeutic strategies for inflammatory and autoimmune conditions:

Targeting Specific TNF-α Forms:

• Selective inhibition of either soluble or transmembrane TNF-α could provide more precise therapeutic effects

• Transmembrane TNF-α-specific agents might retain beneficial immune surveillance while reducing inflammatory damage

• Structure-based design is enabling development of form-selective inhibitors

Timing-Based Intervention Strategies:

• Understanding of biphasic TNF-α responses suggests potential for timed-release therapeutics

• Short-term TNF-α suppression followed by withdrawal might leverage natural regulatory mechanisms

• Chronotherapy approaches could synchronize anti-TNF administration with circadian patterns of inflammation

Combination Approaches:

• Simultaneous targeting of TNF-α and enhancement of Treg function

• Dual-action molecules affecting both the inflammatory and regulatory arms of immunity

• Combination therapies addressing multiple inflammatory pathways with reduced doses of each agent

Precision Medicine Applications:

• Biomarker-guided selection of anti-TNF therapies based on individual immunological profiles

• Genetic testing to predict response to specific anti-TNF agents

• Monitoring of TNF-α dynamics to optimize dosing and treatment intervals

Novel Delivery Systems:

• Site-specific delivery of anti-TNF agents to reduce systemic effects

• Extended-release formulations for improved pharmacokinetics

• Cell-based delivery systems that respond to inflammatory signals

Immune Cell Modulation:

• Leveraging the newly discovered TNF-α/Treg axis to promote resolution of inflammation

• Cellular therapies using Tregs preconditioned with TNF-α for enhanced regulatory function

• Combined approaches that address both acute inflammation and promote long-term immune regulation

These advances suggest a future where anti-TNF therapies move beyond simply blocking TNF-α activity to more sophisticated approaches that selectively modulate specific aspects of TNF-α biology in a context-dependent manner, potentially improving efficacy while reducing adverse effects.