SIRPG Human, Sf9

What is SIRPG and what is its basic structure?

SIRPG (Signal Regulatory Protein Gamma, also known as CD172G) is a transmembrane protein expressed on the surface of lymphocytes that acts by engaging its ligand, CD47 . Structurally, SIRPG belongs to a family of proteins characterized by three extracellular immunoglobulin-like (Ig-like) domains and a single transmembrane domain . Based on comprehensive bioinformatic analyses, SIRPG is one of at least 42 human proteins sharing this characteristic domain structure, with specific subtypes of Ig-like domains (V, C1, C1) followed by a transmembrane domain .

What are the different transcript isoforms of SIRPG and how do they differ?

SIRPG produces multiple transcript isoforms through alternative splicing, all of which encode potentially functional proteins . Four main transcript isoforms have been identified:

• Isoform 1: The canonical transcript using all six exons

• Isoform 2: Uses alternative transcriptional and translational start sites

• Isoform 3: Differs in inclusion/exclusion of exon 3

• Isoform 4: Differs in inclusion/exclusion of exon 4

These isoforms show different expression patterns, with significant genetic control over their relative abundance, particularly associated with the rs6043409 variant which affects exonic splicing enhancers .

What is the Sf9 expression system and why is it used for SIRPG research?

Sf9 is an insect cell line derived from Spodoptera frugiperda that serves as a common host for recombinant protein expression. Researchers utilize Sf9 cells because they can efficiently produce complex mammalian proteins with proper folding and post-translational modifications . For SIRPG and related proteins, Sf9 cells provide a suitable environment for expression studies, as demonstrated with similar proteins like novel FGF receptors which, when produced in Sf9 cells, maintain proper binding characteristics to ligands such as FGF2 .

How can SIRPG be efficiently expressed in Sf9 insect cells?

To express SIRPG in Sf9 insect cells, researchers typically use baculovirus expression vectors. The methodology involves:

• Cloning the SIRPG coding sequence into a baculovirus transfer vector

• Generating recombinant baculovirus through co-transfection with linearized baculovirus DNA

• Amplifying the viral stock through multiple rounds of infection

• Optimizing expression conditions (MOI, time of harvest, temperature)

• Harvesting cells 48-72 hours post-infection when protein expression peaks

For optimal yield, expression parameters including cell density (2×10^6 cells/ml), infection multiplicity, and harvest timing require careful optimization to balance protein quantity with quality.

What purification strategies are effective for SIRPG produced in different expression systems?

Purification of SIRPG typically involves affinity chromatography approaches depending on the tag system used:

• For His-tagged SIRPG (common in both mammalian and Sf9 systems):

  • Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin

  • Wash steps with low imidazole concentrations (10-20 mM)

  • Elution with higher imidazole concentrations (250-300 mM)

• For other tagged versions:

  • Fc-tagged SIRPG can be purified using Protein A/G columns

  • Avi-tagged proteins can be captured on streptavidin matrices if biotinylated

Further purification typically involves size exclusion chromatography to separate monomeric protein from aggregates and enhance purity to >90% as assessed by SDS-PAGE .

How do post-translational modifications of SIRPG differ between human cells and Sf9 expression systems?

SIRPG undergoes glycosylation in human cells that impacts its function. When comparing expression systems:

• Human cell lines (HEK293T, Jurkat):

  • Produce SIRPG with complex N-linked glycosylation

  • Show multiple bands on Western blots representing different glycoforms

  • Treatment with PNGaseF eliminates higher molecular weight bands, confirming glycosylation

• Sf9 insect cells:

  • Produce proteins with simpler, high-mannose glycosylation

  • May show different mobility patterns on SDS-PAGE

  • Still retain core functionality for binding studies

This glycosylation difference must be considered when interpreting binding affinity and functional assays between expression systems, particularly for interaction studies with CD47.

How does SIRPG compare structurally to other members of the signal regulatory protein family?

SIRPG belongs to the signal regulatory protein family and shares structural similarities with related proteins:

What is known about SIRPG-CD47 interaction and its functional consequences?

The interaction between SIRPG and CD47 has several functional consequences:

• Cell-cell adhesion: SIRPG on T cells interacts with CD47 on other cells, facilitating cell-cell conjugates. CRISPR/Cas9 targeting of SIRPG in Jurkat T cells resulted in cells forming fewer cell-cell conjugates than wild-type Jurkat cells .

• Gene expression modulation: The SIRPG-CD47 interaction affects gene expression. Jurkat cells with targeted deletion of SIRPG showed reduced expression of genes associated with CD47 signaling .

• CD47 regulation: SIRPG expression levels impact cell-surface CD47 levels. Cells lacking SIRPG showed significantly increased levels of cell-surface CD47, suggesting a regulatory feedback mechanism .

• T cell activation: Cell-surface SIRPγ levels in response to anti-CD3 stimulation vary quantitatively by rs6043409 genotype, indicating genetic control of this interaction .

How do different SIRPG transcript isoforms affect protein function?

Different SIRPG transcript isoforms encode proteins with potentially distinct functions:

• All four transcript isoforms (1-4) can produce stable proteins when expressed in vitro in HEK293T cells, as demonstrated by detection with V5 epitope tag antibodies .

• Functional differences emerge from structural variations:

  • Isoforms lacking certain exons may have altered binding affinity for CD47

  • CRISPR/Cas9 targeting of one alternatively spliced exon eliminated all SIRPγ expression in Jurkat T cells, indicating its critical importance for protein stability or expression

• Genetic variants like rs6043409 shift the balance between isoforms, with homozygous carriers of the minor A allele showing:

  • Decreased expression of isoforms 1 and 2

  • Increased expression of isoform 3 (though not statistically significant in some studies)

These shifts in isoform distribution correlate with type 1 diabetes risk, suggesting functional consequences of altered isoform ratios.

How does SIRPG genetic variation contribute to type 1 diabetes risk?

SIRPG contains a nonsynonymous coding variant, rs6043409, that is significantly associated with risk for type 1 diabetes . This variant exerts its effects through several mechanisms:

• Splicing regulation: rs6043409 alters a predicted exonic splicing enhancer, resulting in significant shifts in the distribution of SIRPG transcript isoforms .

• Protein expression: In primary CD4+ and CD8+ T cells, cell-surface SIRPγ levels in response to anti-CD3 stimulation vary quantitatively by rs6043409 genotype .

• Functional consequences: The altered expression pattern affects:

  • Cell-cell adhesion capacities

  • Expression of genes associated with CD47 signaling

  • Cell-surface CD47 levels

These findings suggest that SIRPG is the most likely causative gene for type 1 diabetes risk in the 20p13 chromosomal region, highlighting the role of alternative splicing in lymphocytes in mediating genetic risk for autoimmunity .

What experimental approaches have validated SIRPG's role in autoimmune conditions?

Several experimental approaches have established SIRPG's role in autoimmunity:

• Genetic association studies: Identified rs6043409 as significantly associated with type 1 diabetes risk .

• Transcript isoform analysis: Quantitative PCR with isoform-specific probes demonstrated genotype-dependent shifts in SIRPG transcript isoform distribution .

• RNA-seq analysis: Examination of CD4+ and CD8+ T cells from 82 unrelated subjects with type 1 diabetes confirmed genotype-specific differences in isoform abundance .

• CRISPR/Cas9 gene editing: Targeted deletion of alternatively spliced exons in Jurkat T cells eliminated SIRPγ expression and altered:

  • Cell-cell conjugate formation

  • Gene expression profiles

  • CD47 surface levels

• Primary cell studies: Analysis of SIRPγ expression in response to anti-CD3 stimulation in CD4+ and CD8+ T cells from subjects with different rs6043409 genotypes showed quantitative differences in protein expression .

What flow cytometry approaches are optimal for detecting surface SIRPG in primary human T cells?

For optimal detection of surface SIRPG in primary human T cells, the following flow cytometry protocol has proven effective:

• Sample preparation:

  • Thaw PBMCs and resuspend in FACS buffer (PBS without calcium and magnesium, 5 mg/mL BSA, 0.1% NaN₃)

  • Block with TruStain FcX to prevent non-specific antibody binding

  • Stain with directly conjugated antibodies following standard cell-surface staining procedures

• Antibody panel:

  • Anti-SIRPγ (BioLegend)

  • Anti-CD4 (BioLegend)

  • Anti-CD8 (BD Biosciences)

  • 7-AAD viability stain (BioLegend)

• Analysis settings:

  • Exclude dead cells using 7-AAD

  • Gate on lymphocytes based on scatter parameters

  • Identify CD4+ and CD8+ T cell populations

  • Measure SIRPγ expression within these populations

Analysis should be performed with appropriate flow cytometry software such as FlowJo, with comparison of median fluorescence intensity (MFI) values for SIRPγ across different conditions or genotypes .

How can one design effective CRISPR/Cas9 targeting strategies for SIRPG functional studies?

Effective CRISPR/Cas9 targeting strategies for SIRPG functional studies should consider:

• Target selection:

  • Targeting alternatively spliced exons to study isoform-specific functions

  • Selecting exons that encode functional domains for binding studies

  • Avoiding regions with potential off-target effects

• Guide RNA design:

  • Design sgRNAs with minimal off-target potential

  • Target conserved exons for complete knockout

  • Consider targeting regions near splicing junctions to affect specific isoforms

• Validation approaches:

  • Genomic PCR and sequencing to confirm mutations

  • Western blotting to verify protein loss

  • Flow cytometry to confirm loss of surface expression

  • Functional assays to assess phenotypic consequences

• Controls:

  • Include non-targeting sgRNAs as controls

  • Generate multiple knockout clones with different sgRNAs targeting the same gene

  • Consider rescue experiments by re-expressing individual isoforms

This approach successfully eliminated all SIRPγ expression in Jurkat T cells when targeting alternatively spliced exons in SIRPG .

What quantitative methods exist for measuring SIRPG transcript isoform expression?

Several quantitative methods have been employed to measure SIRPG transcript isoform expression:

• Isoform-specific quantitative PCR:

  • Design fluorescently labeled probes specific for each SIRPG transcript isoform

  • Include probes for isoforms 1 and 2, isoform 3, isoform 4, and a probe detecting all isoforms

  • Normalize to appropriate housekeeping genes

  • Compare expression across different genotypes or conditions

• RNA-seq with Event Analysis:

  • Count reads that map uniquely to specific SIRPG transcript isoforms

  • Resolve each of the four SIRPG transcript isoforms

  • Compare their relative abundance between different groups

  • This approach successfully identified statistically significant differences in abundance by rs6043409 genotype

• Protein correlation:

  • Verify transcript expression at the protein level using isoform-specific antibodies

  • Clone individual isoforms into expression vectors with epitope tags (e.g., V5)

  • Transiently transfect into cell lines and detect protein expression via immunoblotting

These methods collectively provide comprehensive analysis of SIRPG isoform expression patterns and can be applied to various experimental conditions and genetic backgrounds.

How does SIRPG compare functionally to other related immune regulatory proteins?

SIRPG belongs to a broader family of structurally related proteins with three Ig-like domains and a single transmembrane domain. When comparing SIRPG with related immune regulatory proteins:

Within the immune regulatory group, SIRPG (Signal-regulatory protein γ) shows functional similarity to SIRPA and SIRPB1 but with distinct roles. While SIRPA contains ITIM domains and provides inhibitory signals, SIRPG lacks these motifs, suggesting different signaling mechanisms . Unlike some other family members involved in pathogen recognition, SIRPG primarily mediates interactions between immune cells through CD47 binding .

What methodologies are effective for studying SIRPG-mediated cell-cell interactions?

To study SIRPG-mediated cell-cell interactions, researchers have employed several effective methodologies:

• Cell conjugate formation assays:

  • Mix SIRPG-expressing cells with CD47-expressing cells labeled with different dyes

  • Measure conjugate formation by flow cytometry

  • Compare wild-type cells to SIRPG-knockout cells

  • This approach revealed that SIRPG-deficient Jurkat T cells formed fewer cell-cell conjugates than wild-type cells

• Cell adhesion strength measurements:

  • Use atomic force microscopy to measure binding forces

  • Apply controlled shear forces to cell pairs

  • Quantify the force required to separate SIRPG-CD47 mediated interactions

• Live cell imaging:

  • Visualize SIRPG localization during immune synapse formation

  • Track recruitment dynamics with fluorescently tagged proteins

  • Measure enrichment at contact interfaces

• Protein complementation assays:

  • Split reporter systems (like split GFP) fused to SIRPG and CD47

  • Signal generated only when proteins interact

  • Allows quantification of interaction strength in living cells

These methods collectively provide insights into the dynamics and functional consequences of SIRPG-mediated cellular interactions in immune regulation.

What are the most promising research directions for SIRPG in autoimmune disease therapy?

Based on current understanding of SIRPG biology, several promising research directions for autoimmune disease therapy emerge:

• Isoform-specific targeting:

  • Development of therapeutic antibodies targeting specific SIRPG isoforms

  • Small molecules that modulate the splicing of SIRPG to favor protective isoform distributions

  • Gene therapy approaches to alter SIRPG isoform expression ratios

• SIRPG-CD47 interaction modulation:

  • Therapeutic agents that selectively enhance or inhibit SIRPG-CD47 interactions

  • Peptide mimetics that compete with SIRPG binding to CD47

  • Engineering soluble SIRPG variants as potential decoy receptors

• Personalized medicine approaches:

  • Stratification of patients based on rs6043409 genotype

  • Tailored therapies accounting for genotype-specific differences in SIRPG expression

  • Combination therapies targeting multiple aspects of SIRPG-related pathways

These approaches highlight the potential of SIRPG as a therapeutic target, with particular relevance to type 1 diabetes and potentially other autoimmune conditions where T cell dysregulation plays a key role.

How can comparative expression of SIRPG in different systems inform therapeutic development?

Comparative expression studies of SIRPG across different systems provide crucial insights for therapeutic development:

• Expression system selection:

  • HEK293 cells provide mammalian glycosylation patterns but may have limited yield

  • Sf9 insect cells offer higher protein yields but with different post-translational modifications

  • The choice impacts structural and functional characteristics relevant for drug screening

• Isoform-specific effects:

  • All four SIRPG transcript isoforms produce stable proteins in expression systems

  • Different isoforms may exhibit varied binding properties to therapeutic candidates

  • Comprehensive screening should include all relevant isoforms

• Translation to clinical applications:

  • Correlation between in vitro expression systems and primary human T cells

  • Verification that therapeutic candidates maintain activity across expression platforms

  • Consideration of glycosylation and other modifications in therapeutic efficacy