FAS Human, His

What is the gene structure and chromosomal location of human FAS?

The human FAS gene spans approximately 8.0 kb and is organized into four exons. Through in situ hybridization studies against human metaphase chromosomes, researchers have localized the gene to chromosome 1q23 . The human FAS ligand (FasL) is a type II membrane protein consisting of 281 amino acids with a calculated molecular weight of 31,759 Da. Comparison of human and mouse FasL chromosomal genes reveals a highly conserved ~300 bp sequence upstream of the ATG initiation codon, containing several transcription cis-regulatory elements including SP-1, NF-κB, and IRF-1 binding sites . This conservation suggests functional importance in the regulation of FAS gene expression across species.

How is recombinant human Fas protein with His tag typically expressed and what are its specifications?

Recombinant human Fas protein with His tag is commonly expressed using the human 293 cells (HEK293) expression system. The typical commercial preparation contains amino acids Gln 26 - Asn 173 (according to Accession # AAH12479.1) . The resulting protein has a molecular weight of approximately 17.5 kDa and requires storage at -20°C to maintain stability . This expression system is preferred because it allows for proper post-translational modifications that may be important for the protein's functionality. The His tag facilitates purification using metal affinity chromatography and can be used for detection in experimental procedures without significantly altering the protein's structure or function.

What are the primary functional domains of the Fas protein and how do they relate to its role in apoptosis?

The Fas receptor contains a death domain (DD) in its cytoplasmic region that is critical for signal transduction during apoptosis. Upon binding of Fas ligand, the receptor undergoes trimerization, which leads to aggregation of the death domains . This aggregation facilitates the formation of the death-inducing signaling complex (DISC), which is subsequently internalized via the cellular endosomal machinery . The extracellular portion contains cysteine-rich domains typical of the TNF receptor superfamily, which are involved in ligand recognition. The death domain serves as a docking site for adapter proteins such as FADD (Fas-associated death domain), which in turn recruits procaspase-8, initiating the caspase cascade that ultimately leads to programmed cell death. Understanding these structural elements is essential for designing experiments to study Fas-mediated apoptosis pathways.

What methodologies are most effective for analyzing Fas expression at protein versus gene levels?

For protein-level analysis of Fas expression, immunohistochemistry techniques using the Human Protein Atlas resources have been successfully employed across 578 samples from various tissues . This approach provides visual confirmation of protein localization and expression intensity. For quantitative assessment, normalized transcripts per million (nTPM) values can be extracted from datasets to measure FAS gene expression levels .

For gene expression analysis, multiple databases including GENT2, GEPIA2, and UALCAN provide comprehensive tools for mining FAS expression data . In vitro validation of FAS gene expression is commonly performed on cell lines such as H1299, H1993, A549, and HBE . When designing experiments to study Fas expression, researchers should consider both transcriptional regulation (using RT-PCR or RNA-seq) and protein expression (using Western blot, flow cytometry, or immunohistochemistry) to obtain a complete picture of Fas biology in their experimental system.

How does Fas expression correlate with cell cycle status and what are the implications for experimental design?

Studies have established a strong correlation between Fas expression and cell cycle status, particularly in hematopoietic stem cells. Fas expression is up-regulated when HSCs enter active cycling phases . This has significant implications for experimental design, as researchers must carefully consider the proliferation state of cells when interpreting Fas expression data.

For experiments involving cell cycle analysis alongside Fas expression, methodologies should include:

• Cell cycle synchronization techniques

• BrdU incorporation assays to mark cells in S-phase

• Propidium iodide staining for DNA content analysis

• Concurrent flow cytometric analysis of Fas expression and cell cycle markers

Importantly, despite expressing high levels of Fas, reconstituting HSCs remain highly resistant to Fas-mediated suppression, and HSC function is compromised only upon coactivation with tumor necrosis factor . This suggests that additional regulatory mechanisms beyond mere Fas expression determine cellular susceptibility to Fas-mediated apoptosis, which should be accounted for in experimental designs.

What are the key protein-protein interaction networks involving Fas and how can they be studied?

Protein-protein interaction networks involving Fas can be reconstructed using databases such as STRING and GeneMANIA . STRING integrates known and predicted protein-protein interactions from multiple sources, including experimental data, computational prediction methods, and text mining. Interaction confidence scores are based on the strength of evidence . GeneMANIA provides predictions using functional genomics data, including co-expression, colocalization, and physical interaction data .

To experimentally study these interactions, researchers commonly employ:

• Co-immunoprecipitation followed by mass spectrometry

• Proximity ligation assays for in situ detection

• Fluorescence resonance energy transfer (FRET) for real-time interaction analysis

• Yeast two-hybrid screening for novel interacting partners

When designing such experiments, researchers should be aware that while both STRING and GeneMANIA employ machine-learning algorithms to predict novel interactions, predictions can sometimes be prone to false positives or depend on incomplete datasets . Therefore, computational predictions should be validated with wet-lab experiments for confirmation.

How can co-expression analysis be utilized to identify genes functionally related to FAS?

Co-expression analysis of genes with FAS can be performed using platforms such as GeneMANIA and UALCAN. GeneMANIA employs a combination of Pearson correlation coefficients and other statistical methods to identify genes displaying similar expression patterns, which are then visualized in a network . For more comprehensive visualization, UCSC Xena can generate correlation heat maps using TCGA datasets .

To effectively utilize co-expression analysis:

• Begin with Pearson correlation to measure the strength of co-expression between FAS and associated genes

• Establish statistical significance thresholds appropriate for dataset size

• Generate network visualizations to identify clusters of functionally related genes

• Validate co-expression patterns across multiple independent datasets

• Perform pathway enrichment analysis on co-expressed gene clusters to identify biological processes

This methodological approach allows researchers to move beyond simple correlations to identify gene networks that may be functionally related to FAS signaling, potentially revealing novel therapeutic targets or regulatory mechanisms.

What is the prognostic significance of FAS gene expression in cancer and how should researchers analyze this data?

The prognostic significance of FAS in cancer, particularly lung cancer, can be assessed using the OSluca web server, which performs hazard ratio (HR) analysis of data from various datasets such as TCGA and GEO . When analyzing such data, researchers should:

This methodological approach provides a more robust assessment of FAS as a prognostic marker than analysis of single datasets. When interpreting these results, researchers should consider potential confounding factors such as cancer subtype, treatment history, and patient demographics that might influence the relationship between FAS expression and clinical outcomes.

How does Fas resistance develop in cancer cells despite Fas expression, and what experimental approaches can investigate this phenomenon?

While many cancer cells express Fas, they often develop resistance to Fas-mediated apoptosis through various mechanisms. Investigating this phenomenon requires multifaceted experimental approaches:

• Receptor functionality assessment: Flow cytometry with fluorescent-labeled Fas ligand to determine binding capacity

• Downstream signaling analysis: Western blotting for DISC formation components and activated caspases

• Inhibitor protein measurement: Quantification of c-FLIP, Bcl-2 family proteins, and IAPs that may block apoptosis

• Genetic screening: CRISPR/Cas9 screens to identify novel regulators of Fas sensitivity

• Drug sensitization assays: Testing combinations of Fas-activating agents with inhibitors of anti-apoptotic proteins

Research has shown that in some contexts like hematopoietic stem cells, despite high Fas expression, cells remain resistant to Fas-mediated suppression unless co-activated with tumor necrosis factor . This suggests that resistance mechanisms may involve additional regulatory layers beyond the Fas receptor itself, possibly including alterations in the threshold for activating the apoptotic machinery or compensatory survival signaling pathways.

What are the essential elements of research design when studying Fas-mediated pathways?

When designing research on Fas-mediated pathways, several key principles should be incorporated:

• Validity: Select appropriate measuring tools to gauge results according to the research objective . For Fas studies, this might include validated apoptosis assays, standardized flow cytometry protocols for Fas expression, or established protein interaction assays.

• Generalizability: Design experiments so outcomes can be applied to a large set of conditions and are not limited to the specific sample or research group . This requires sufficient biological replicates and consideration of cell type-specific responses.

• Neutrality: Make testable assumptions at the start of research . For Fas studies, clarify hypotheses about expected expression patterns or functional outcomes before beginning experiments.

• Problem identification: Clearly define the research question regarding Fas function or expression before selecting methodologies .

• Literature review: Comprehensively review existing literature on Fas biology relevant to the research question .

• Hypothesis specification: Formulate specific hypotheses about Fas-mediated processes based on preliminary data and literature .

• Data source description: Detail the biological materials, technologies, and analytical tools to be used .

• Data interpretation framework: Establish in advance how results will be interpreted in the context of existing knowledge about Fas biology .

How should researchers design experiments to investigate contradictory findings about Fas function in different cell types?

When faced with contradictory findings about Fas function across different cell types, researchers should implement a systematic experimental design approach:

• Direct comparison: Design experiments that simultaneously examine multiple cell types under identical conditions to directly compare Fas responses.

• Controlled variables: Meticulously control for variables such as culture conditions, passage number, activation status, and cell density that might influence Fas responsiveness.

• Comprehensive phenotyping: Characterize each cell type for expression of not just Fas but also downstream signaling components and inhibitory proteins.

• Temporal analysis: Investigate the kinetics of Fas-mediated signaling, as timing differences might explain contradictory outcomes.

• Context-dependent factors: Systematically test how microenvironmental factors (cytokines, growth factors, cell-cell interactions) modulate Fas responses in each cell type.

• Genetic manipulation: Use CRISPR/Cas9 or RNAi approaches to modify specific components of the Fas pathway to determine which elements contribute to differential responses.

• Multi-omics approach: Integrate transcriptomics, proteomics, and metabolomics data to obtain a systems-level view of Fas signaling networks in each cell type.

This approach acknowledges that Fas function can vary dramatically between contexts – for example, while Fas is upregulated in cycling hematopoietic stem cells, these cells remain resistant to Fas-mediated apoptosis unless co-stimulated with TNF , a finding that might appear contradictory without considering the broader signaling context.

What quality control metrics should be implemented when working with recombinant His-tagged Fas proteins?

When working with recombinant His-tagged Fas proteins, implementing rigorous quality control metrics is essential for experimental reproducibility:

Commercial preparations of Human Fas, His Tag (such as product FAS-H5229) are typically expressed in human 293 cells (HEK293) and contain amino acids Gln 26 - Asn 173 . Researchers should verify these specifications and implement appropriate storage conditions (-20°C is typically recommended ) to maintain protein integrity throughout experimental procedures.

How does the His-tag affect Fas protein functionality and what controls should be included in experiments?

The His-tag on Fas protein can potentially influence protein folding, oligomerization, or receptor-ligand interactions. To address these concerns, researchers should implement the following controls and considerations:

• Tag position evaluation: Compare N-terminal versus C-terminal His-tagged versions to determine if tag position affects function

• Tag-free controls: Include tag-free Fas protein preparations as positive controls when possible

• Tag cleavage: Utilize protease cleavage sites (e.g., TEV, thrombin) between the tag and protein to remove the tag after purification

• Functional comparison: Directly compare the apoptosis-inducing capacity of tagged versus untagged protein

• Binding affinity assessment: Measure if the His-tag alters binding kinetics to FasL using surface plasmon resonance

• Oligomerization analysis: Employ analytical ultracentrifugation or native PAGE to assess if the tag affects Fas trimerization

Research has demonstrated that membrane-anchored Fas ligand trimer on the surface of an adjacent cell causes trimerization of Fas receptor . Experiments should verify that His-tagged Fas maintains this capability for proper signal transduction.

What are the optimal methods for quantifying Fas-mediated apoptosis in experimental systems?

Quantifying Fas-mediated apoptosis requires a multi-parameter approach to capture the various aspects of the apoptotic process:

For robust quantification, researchers should combine at least three independent methods, with priority given to methods that specifically detect events in the extrinsic apoptosis pathway initiated by Fas. When interpreting results, it's important to consider that some cell types (like certain hematopoietic stem cells) may express Fas but remain resistant to Fas-mediated apoptosis unless co-stimulated with other factors .

What bioinformatic approaches are most effective for analyzing large-scale data related to FAS expression and function?

For analyzing large-scale data related to FAS expression and function, several bioinformatic approaches have proven effective:

• Co-expression network analysis:

  • Tools: GeneMANIA, WGCNA (Weighted Gene Co-expression Network Analysis)

  • Application: Identifies modules of genes with similar expression patterns to FAS

  • Implementation: Use Pearson correlation and hierarchical clustering to define modules

• Survival analysis integration:

  • Tools: OSluca web server, Kaplan-Meier Plotter

  • Application: Assesses prognostic significance across multiple datasets

  • Implementation: Generate hazard ratios with 95% confidence intervals and apply FDR correction

• Multi-omics data integration:

  • Tools: iCluster, Similarity Network Fusion

  • Application: Integrates FAS-related data across transcriptomics, proteomics, and epigenomics

  • Implementation: Identify convergent patterns across different data types

• Protein interaction prediction and validation:

  • Tools: STRING, GeneMANIA

  • Application: Reconstructs interaction networks around FAS

  • Implementation: Combine experimental data, computational predictions, and text mining

• Pathway enrichment analysis:

  • Tools: Enrichr, GSEA, Reactome

  • Application: Identifies biological processes associated with FAS expression patterns

  • Implementation: Test for statistical overrepresentation of gene sets in expression data

When applying these approaches, researchers should be aware that bioinformatic predictions require experimental validation, as computational methods can sometimes generate false positives or depend on incomplete datasets . Integration of multiple independent datasets improves statistical power and reliability of findings.