Recombinant Human Tigger transposable element-derived protein 3 (TIGD3)

What is TIGD3 and where is it located in the human genome?

TIGD3 (tigger transposable element derived 3) is a protein-coding gene belonging to the tigger subfamily of the pogo superfamily of DNA-mediated transposons in humans. These proteins are related to DNA transposons found in fungi and nematodes, and more distantly to the Tc1 and mariner transposases. They show significant similarity to the major mammalian centromere protein B .

The TIGD3 gene is located on chromosome 11 at position q13.1 (11q13.1). The genomic coordinates on chromosome 11 are 65354751 to 65357613 (NC_000011.10). The gene consists of 2 exons in total .

What is known about the molecular weight and structure of TIGD3?

Western blot analysis using anti-TIGD3 antibody (In-Cell-Art, Nantes, France) has identified TIGD3 as a protein with a molecular weight of 52 kDa. This molecular weight is consistent with the expected size of the TIGD3 transposase . The protein's complete three-dimensional structure has not been fully characterized in the available research, though its classification within the tigger subfamily suggests structural similarities to other transposase proteins.

What are the basic experimental methods for detecting TIGD3 expression?

Detection of TIGD3 expression can be accomplished through several established methods:

• Western Blot Analysis: Using anti-TIGD3 antibodies to detect the 52 kDa TIGD3 protein, as demonstrated in colorectal cancer research .

• Immunohistochemical Staining: This method has been effectively used to visualize TIGD3 expression in tissue samples, allowing for correlation with pathological features .

• Fluorescent Microscopy: TIGD3 tagged with fluorescent proteins (such as mScarlet) can be visualized to determine subcellular localization and co-localization with other cellular structures, as demonstrated in the study of its association with Human Satellite 3 DNA .

• Protein Expression Systems: For recombinant TIGD3 production, various systems have been successfully employed, including:

  • FLiP-in HEK293 cells induced with Doxycycline

  • PURExpress T7 recombinant in vitro transcription/translation systems

  • SP6-driven wheat germ extract-based in vitro transcription/translation

What is the relationship between TIGD3 expression and cancer progression?

Research has revealed significant correlations between TIGD3 expression and cancer progression, particularly in colorectal cancer. A cross-sectional study of 100 colorectal cancer samples revealed the following associations:

Table 1: TIGD3 Expression in Colorectal Cancer

The study demonstrated that TIGD3 expression increases progressively from normal colon tissue to advanced stages of colorectal cancer. Western blot analysis confirmed this pattern, showing minimal expression in normal colon tissue but significantly higher expression in stage I-III colorectal cancer tissues, with expression levels increasing with disease progression .

The elevated TIGD3 expression appears to promote cell proliferation, delay apoptosis, and increase tumor aggressiveness, contributing to metastatic potential. Importantly, TIGD3 expression was found to be independent of age, tumor size, gender, tumor location, mucinous component, pathological tumor stage, and differentiation .

How does TIGD3 interact with Human Satellite 3 DNA, and what are the implications?

TIGD3 has been identified as a factor that co-localizes with Human Satellite 3 (HSat3) DNA foci, as demonstrated through fluorescent microscopy studies. When TIGD3-mScarlet was transiently transfected with ZFsat3-megfp (a marker for HSat3), clear co-localization was observed, confirming TIGD3's interaction with HSat3 regions .

This interaction is significant because HSat3 DNA has been found to encode megabase-scale transcription factor binding regions. The research demonstrates that satellite DNA, previously considered "junk DNA," can encode multiple transcription factor binding motifs, defining a new role for these enormous genomic elements .

The co-localization pattern suggests that TIGD3 may play a role in the regulation of these satellite DNA regions, potentially affecting gene expression or chromosomal organization. This finding opens new avenues for understanding how transposable element-derived proteins like TIGD3 may influence genome function through interaction with repetitive DNA elements.

What methodological approaches are most effective for studying TIGD3's function in cancer models?

Based on current research, several methodological approaches have proven effective for studying TIGD3's function in cancer:

• Combined Western Blot and Immunohistochemistry Analysis: This dual approach provides both quantitative data on expression levels and spatial information on protein localization within tissues. In colorectal cancer research, this combination successfully demonstrated the correlation between TIGD3 expression and cancer progression .

• Clinical-Pathological Correlation Studies: Comparing TIGD3 expression levels with detailed clinical and pathological data has revealed significant associations with disease stage and invasive features. This approach is particularly valuable for identifying potential prognostic biomarkers .

• Protein-DNA Interaction Studies: Techniques such as DiMeLo-seq, which maps protein-DNA interactions by recruiting adenine DNA methyltransferase to a target protein, can be adapted to study TIGD3's binding patterns. While not specifically used for TIGD3 in the available studies, this approach was successfully applied to study other transcription factors that co-localize with similar genomic regions .

• Multiple Experimental Platform Approach: The Codebook project demonstrated that no single assay type or data analysis approach was universally successful for studying transcription factors. For comprehensive characterization of TIGD3's function, combining multiple experimental strategies (such as ChIP-seq, SELEX variants, and PBMs) with multiple motif-derivation and motif-scoring strategies is recommended .

What challenges exist in the production of recombinant TIGD3 for research purposes?

Production of recombinant TIGD3 presents several challenges that researchers should consider:

• Expression System Selection: Different expression systems can differentially impact the success of specific DNA-binding domain classes. For TIGD3, researchers have options including:

  • Mammalian cell expression (FLiP-in HEK293 cells)

  • Bacterial cell-free expression (PURExpress T7 recombinant IVT system)

  • Plant-based cell-free expression (SP6-driven wheat germ extract)

• Protein Purification Considerations: The choice of affinity tags and purification methods can affect protein yield and activity. The purification approach needs to be optimized specifically for TIGD3 to maintain its structural integrity and functional properties.

• Functional Validation: Given TIGD3's role in DNA binding, functional assays such as electrophoretic mobility shift assays (EMSA) or DNA footprinting should be considered to confirm that the recombinant protein retains its native DNA binding properties.

• Protein Folding and Stability: As a member of the tigger transposase family, TIGD3 may have specific folding requirements. Expression conditions may need to be optimized to ensure proper protein folding and stability.

• Confounding Variables: When comparing different experimental approaches, researchers should be aware that protein production and purification methods can impact results, even when the same assay is used. Data pre-processing (including read filtering and background estimation) is an additional variable that can impact all assays .

What is the potential of TIGD3 as a prognostic biomarker in cancer?

Current research strongly suggests that TIGD3 has significant potential as a prognostic biomarker in cancer, particularly colorectal cancer. The following evidence supports this application:

• Correlation with Advanced Disease: TIGD3 expression has been found to correlate with higher stages of cancer (TNM III-IV), suggesting its utility in identifying patients with more advanced disease .

• Association with Invasive Features: Significant correlations between TIGD3 expression and both lymph vascular invasion (p=0.000045) and angiovascular invasion (p=0.00001) indicate that TIGD3 may serve as a marker for tumors with greater invasive potential .

• Progression Marker: The observation that TIGD3 expression increases progressively from normal tissue to advanced cancer suggests it could be valuable for monitoring disease progression or treatment response .

To establish TIGD3 as a clinical biomarker, future research should focus on:

• Validating these findings in larger, multi-center cohorts

• Determining specific expression thresholds that correlate with clinical outcomes

• Developing standardized detection methods suitable for clinical laboratories

• Investigating whether TIGD3 expression can predict response to specific therapy options

How might understanding TIGD3's interaction with Human Satellite 3 DNA lead to new therapeutic approaches?

The discovery that TIGD3 interacts with Human Satellite 3 DNA opens novel therapeutic possibilities that warrant further investigation:

• Targeting Satellite DNA Regulation: If TIGD3's interaction with HSat3 contributes to cancer progression, developing compounds that disrupt this interaction could represent a novel therapeutic strategy.

• Exploiting Transcription Factor Networks: Research shows that satellite DNA can encode multiple transcription factor binding motifs. Understanding how TIGD3 participates in these networks could reveal vulnerable nodes for therapeutic intervention .

• Epigenetic Modulation: Given that satellite DNA regions are often regulated by epigenetic mechanisms, exploring how TIGD3 might influence epigenetic states could lead to applications of epigenetic therapies in cancers with high TIGD3 expression.

• Targeted Degradation Approaches: Emerging technologies like PROTACs (proteolysis targeting chimeras) could be adapted to specifically target TIGD3 for degradation in cancer cells where it's overexpressed.

Future research should focus on elucidating the precise molecular mechanisms through which TIGD3 interacts with HSat3 and how this interaction influences cancer cell behavior.

What experimental approaches should be considered for investigating TIGD3's mechanism of action in promoting cancer progression?

To elucidate TIGD3's mechanism of action in cancer progression, researchers should consider the following experimental approaches:

• Functional Genomics Screening: CRISPR-Cas9 knockout or knockdown studies of TIGD3 in cancer cell lines would help establish causality between TIGD3 expression and malignant phenotypes.

• Transcriptome Analysis: RNA-seq comparing TIGD3-high versus TIGD3-low cancer cells could identify downstream gene expression changes, illuminating pathways through which TIGD3 promotes cancer progression.

• Chromatin Immunoprecipitation Sequencing (ChIP-seq): This would map TIGD3 binding sites genome-wide, revealing potential direct target genes and confirming its interaction with satellite DNA regions .

• Protein Interaction Studies: Techniques such as co-immunoprecipitation followed by mass spectrometry could identify TIGD3's protein binding partners, providing insights into the molecular complexes through which it functions.

• Animal Models: Developing transgenic mouse models with inducible TIGD3 expression could help validate in vitro findings and provide platforms for testing therapeutic approaches.

• DiMeLo-seq Adaptation: This technique, which has been used to map protein-DNA interactions for other factors, could be adapted specifically for TIGD3 to precisely map its genomic binding sites, especially in repetitive regions like satellite DNA .

• Multi-omics Integration: Combining data from genomics, transcriptomics, proteomics, and epigenomics approaches would provide a comprehensive understanding of TIGD3's role in cancer.

What are the best practices for validating TIGD3 antibodies for research applications?

Proper validation of TIGD3 antibodies is crucial for obtaining reliable research results. Based on current literature and standard antibody validation practices, researchers should:

• Western Blot Validation: Confirm antibody specificity by detecting a single band at the expected molecular weight of 52 kDa for TIGD3 . Compare signals between samples known to express TIGD3 (e.g., advanced colorectal cancer tissue) and those with minimal expression (normal colon tissue).

• Knockout/Knockdown Controls: Validate antibody specificity using TIGD3 knockout or knockdown models to confirm the absence or reduction of signal.

• Recombinant Protein Control: Test antibody against purified recombinant TIGD3 protein as a positive control.

• Cross-reactivity Testing: Ensure the antibody doesn't cross-react with other tigger family proteins or related transposases.

• Immunohistochemistry Optimization: For IHC applications, optimize antigen retrieval methods, antibody concentration, and incubation conditions using positive and negative control tissues.

• Fluorescence Applications: For co-localization studies, such as those with HSat3, validate that antibody specificity is maintained under immunofluorescence conditions .

• Batch Testing: Different antibody lots may vary in performance; maintain consistent validation protocols across batches.

How can researchers effectively design experiments to study TIGD3's role in the context of both cancer progression and genomic stability?

Designing effective experiments to study TIGD3's dual roles requires careful consideration of both oncological and genomic stability aspects:

• Cell Line Selection:

  • Use a panel of cell lines representing different cancer stages and normal controls

  • Include cell lines with known genomic instability phenotypes

  • Consider using matched normal-tumor cell lines from the same patient

• Modulation of TIGD3 Expression:

  • Develop stable cell lines with inducible TIGD3 overexpression or knockdown

  • Use transient transfection for acute effects studies

  • Consider CRISPR-Cas9 genome editing for complete knockout studies

• Functional Readouts:

  • Cancer Progression Metrics: Proliferation, migration, invasion, anchorage-independent growth, and resistance to apoptosis

  • Genomic Stability Metrics: Chromosomal abnormalities, micronuclei formation, DNA damage markers, and mutation rates

  • Satellite DNA Interaction: Co-localization with HSat3, effects on satellite DNA transcription, and satellite repeat stability

• Temporal Considerations:

  • Short-term studies for immediate effects on signaling pathways

  • Long-term studies for accumulated genomic instability effects

  • Cell cycle-synchronized experiments to detect phase-specific effects

• Multimodal Imaging:

  • Combine fluorescent tagging of TIGD3 with markers for satellite DNA regions

  • Use live-cell imaging to track dynamic interactions

  • Implement super-resolution microscopy for detailed co-localization analysis

• Integrated Genomics Approach:

  • Combine ChIP-seq for binding sites with RNA-seq for expression changes

  • Incorporate whole-genome sequencing to detect structural variations

  • Use Hi-C or similar techniques to detect changes in 3D genome organization

• In Vivo Validation:

  • Develop mouse models with tissue-specific TIGD3 modulation

  • Use patient-derived xenografts to validate findings in a more clinically relevant context

What are the critical controls needed when performing TIGD3 binding experiments with satellite DNA?

When investigating TIGD3 binding to satellite DNA, particularly Human Satellite 3 (HSat3), the following critical controls should be implemented:

• Negative Binding Controls:

  • Include non-HSat3 binding proteins (such as FOXA1, which showed only 1.59-fold enrichment compared to TIGD3's 6.25-fold enrichment in HSat3 methylation)

  • Use mutant TIGD3 with disrupted DNA binding domains

  • Include non-satellite DNA regions as binding targets

• Positive Binding Controls:

  • Include known HSat3-binding proteins such as JRK (which was shown to co-localize with HSat3 foci)

  • Use validated TEAD1-4 proteins that demonstrate strong co-localization with ZFsat3

• Binding Specificity Controls:

  • Perform competition assays with unlabeled satellite DNA

  • Conduct binding assays with scrambled satellite DNA sequences

  • Test binding to related but distinct satellite DNA families

• Methodological Controls for DiMeLo-seq:

  • Include control cells without the DNA methyltransferase fusion protein

  • Verify enrichment at known binding sites for validation (as demonstrated with TEAD1 showing clear enrichment at known TEAD chip-seq peaks)

  • Compare methylation patterns between target protein and control proteins

• Imaging Controls for Co-localization:

  • Use appropriate channel bleed-through controls

  • Include single-fluorophore controls for spectral unmixing

  • Quantify co-localization using established metrics (Pearson's correlation, Manders' overlap coefficient)

• Expression Level Controls:

  • Ensure comparable expression levels of tagged proteins

  • Use both N-terminal and C-terminal tags to rule out tag interference

  • Include untagged protein controls with antibody detection

• Technical Replication:

  • Perform experiments with multiple technical and biological replicates

  • Use different detection methods to confirm binding (e.g., ChIP-seq, DiMeLo-seq, and fluorescence microscopy)

What are common challenges in detecting low-level TIGD3 expression in normal tissues, and how can they be overcome?

Detecting low-level TIGD3 expression in normal tissues presents several challenges, as research has shown that TIGD3 expression is very low or absent in normal tissues compared to cancer samples . Researchers can overcome these challenges through:

• Enhanced Sensitivity Western Blotting:

  • Use high-sensitivity ECL substrates

  • Implement longer exposure times

  • Increase protein loading amounts for normal tissues

  • Consider using antibody signal amplification systems

• Optimized Immunohistochemistry Protocols:

  • Employ signal amplification techniques (e.g., tyramide signal amplification)

  • Optimize antigen retrieval methods specifically for low-abundance proteins

  • Use automated staining platforms for consistent results

  • Extend primary antibody incubation times (overnight at 4°C)

• Quantitative PCR Approaches:

  • Use digital PCR for absolute quantification of low-abundance transcripts

  • Implement nested PCR strategies for increased sensitivity

  • Select appropriate reference genes for normalization in normal tissues

  • Design primers spanning exon-exon junctions to improve specificity

• Enrichment Strategies:

  • Perform subcellular fractionation to enrich for nuclear proteins

  • Use immunoprecipitation to concentrate TIGD3 before detection

  • Consider laser capture microdissection to isolate specific cell types within normal tissues

• Alternative Detection Methods:

  • Implement in situ hybridization for mRNA detection

  • Consider RNAscope technology for single-molecule detection

  • Use mass spectrometry-based proteomics with targeted approaches

• Standardized Controls:

  • Include positive controls with known low TIGD3 expression levels

  • Use recombinant TIGD3 protein dilution series to establish detection limits

  • Implement spike-in controls to quantify recovery efficiency

• Data Analysis Considerations:

  • Use appropriate background subtraction methods

  • Implement digital image analysis for quantification

  • Consider statistical approaches designed for low-abundance targets

How should researchers address data inconsistencies when studying TIGD3 across different cancer types?

When encountering data inconsistencies in TIGD3 studies across different cancer types, researchers should implement the following systematic approach:

• Standardization of Methodologies:

  • Use consistent antibodies and detection methods across studies

  • Standardize protein extraction and quantification protocols

  • Implement uniform cutoff values for defining "positive" expression

  • Develop standard operating procedures for each experimental technique

• Sample Considerations:

  • Account for tumor heterogeneity through multiple sampling within tumors

  • Consider patient demographics and clinical characteristics as confounding variables

  • Document and control for pre-analytical variables (fixation time, storage conditions)

  • Use patient-matched normal tissues as controls when possible

• Technical Validation:

  • Validate findings using multiple detection methods (e.g., Western blot, IHC, qPCR)

  • Confirm antibody specificity in each cancer type studied

  • Implement blinded assessment of staining results

  • Include inter-laboratory validation for critical findings

• Biological Context Analysis:

  • Consider tissue-specific factors that might influence TIGD3 function

  • Evaluate the status of related pathways in different cancer types

  • Assess genetic and epigenetic alterations specific to each cancer type

  • Analyze TIGD3 in the context of cancer molecular subtypes

• Statistical Approaches:

  • Calculate and report effect sizes, not just p-values

  • Use appropriate statistical tests for data distribution

  • Implement multivariate analysis to identify confounding variables

  • Consider meta-analysis approaches when comparing across studies

• Reporting Standards:

  • Document all experimental conditions in detail

  • Report negative or contradictory results

  • Provide raw data and analysis scripts when possible

  • Follow ARRIVE guidelines for animal studies and REMARK guidelines for biomarker studies

• Cross-Cancer Type Comparisons:

  • Analyze TIGD3 expression using pan-cancer datasets like TCGA

  • Implement pathway analysis across cancer types

  • Consider evolutionary conservation of TIGD3 function

  • Evaluate the role of TIGD3 in relation to universal cancer hallmarks

What are priority research questions regarding TIGD3's role in human disease beyond cancer?

While current research has focused primarily on TIGD3's role in cancer, several priority research questions should be explored regarding its potential functions in other human diseases:

• Neurodegenerative Disorders:

  • Does TIGD3 play a role in genomic stability in post-mitotic neurons?

  • Is TIGD3 expression altered in neurodegenerative conditions associated with DNA damage?

  • Could TIGD3's interaction with satellite DNA affect neuronal gene expression programs?

• Inflammatory and Autoimmune Diseases:

  • Is TIGD3 involved in the regulation of inflammatory gene expression programs?

  • Does TIGD3 contribute to the genomic instability observed in some autoimmune conditions?

  • Are there associations between TIGD3 variants and autoimmune disease susceptibility?

• Developmental Disorders:

  • What is TIGD3's expression pattern during embryonic development?

  • Could dysregulation of TIGD3 contribute to congenital abnormalities?

  • Does TIGD3 interact with developmental transcription factors through satellite DNA regions?

• Aging-Related Conditions:

  • Does TIGD3 expression change with aging?

  • Is TIGD3 involved in the genomic instability associated with cellular senescence?

  • Could TIGD3 modulation affect age-related satellite DNA dysregulation?

• Metabolic Disorders:

  • Is TIGD3 expression regulated by metabolic signals?

  • Does TIGD3 affect gene expression programs related to metabolism?

  • Are there correlations between TIGD3 expression and metabolic disease biomarkers?

• Cardiovascular Diseases:

  • Does TIGD3 play a role in vascular cell proliferation or migration?

  • Is TIGD3 expression altered in atherosclerotic plaques?

  • Could TIGD3's potential effects on genomic stability affect cardiac remodeling?

• Reproductive Disorders:

  • Is TIGD3 involved in gametogenesis, where genomic stability is crucial?

  • Does TIGD3 affect placental development or function?

  • Are there associations between TIGD3 variants and fertility issues?

How might technological advances in genomics and proteomics enhance our understanding of TIGD3 function?

Emerging technologies in genomics and proteomics offer promising opportunities to deepen our understanding of TIGD3 function:

• Long-Read Sequencing Technologies:

  • Enable accurate mapping of TIGD3 binding sites in repetitive regions like satellite DNA

  • Allow detection of structural variations associated with TIGD3 dysregulation

  • Facilitate complete transcriptome analysis to identify novel TIGD3 isoforms

• Single-Cell Multi-omics:

  • Reveal cell-specific expression patterns of TIGD3 within heterogeneous tissues

  • Identify rare cell populations with distinct TIGD3 functional states

  • Map cellular trajectories associated with changes in TIGD3 expression

• CRISPR-Based Technologies:

  • CRISPRa/CRISPRi for precise modulation of TIGD3 expression

  • Base editing to create specific TIGD3 variants

  • CRISPR screens to identify synthetic lethal interactions with TIGD3

• Spatial Transcriptomics and Proteomics:

  • Map TIGD3 expression patterns within tissue architectural context

  • Correlate TIGD3 expression with microenvironmental features

  • Identify spatial associations between TIGD3 and interacting partners

• Protein Structure Technologies:

  • AlphaFold and similar AI approaches to predict TIGD3 structure

  • Cryo-EM for determining TIGD3 complexes with DNA and protein partners

  • Hydrogen-deuterium exchange mass spectrometry to map TIGD3 interaction surfaces

• Dynamic Interaction Mapping:

  • BioID or APEX proximity labeling to identify TIGD3 protein interaction networks

  • Live-cell imaging with optogenetic control of TIGD3 activity

  • Real-time tracking of TIGD3 movement during cell cycle progression

• Epigenomic Profiling Technologies:

  • CUT&Tag for high-resolution mapping of TIGD3 binding sites

  • NOMe-seq to assess nucleosome occupancy at TIGD3 binding sites

  • Multi-modal chromatin profiling to understand TIGD3's impact on chromatin states

• DiMeLo-seq Adaptations:

  • Further refinement of this technique, which has already shown promise in mapping protein-DNA interactions in repetitive regions, specifically for TIGD3 studies

  • Integration with other genomic datasets for comprehensive understanding

What interdisciplinary approaches might yield new insights into TIGD3 biology?

Interdisciplinary approaches combining expertise from different fields could significantly advance TIGD3 research:

• Evolutionary Biology + Molecular Biology:

  • Trace the evolutionary history of TIGD3 as a transposable element-derived protein

  • Compare TIGD3 function across species to identify conserved and divergent roles

  • Study how TIGD3's interaction with satellite DNA relates to genome evolution

• Computational Biology + Structural Biology:

  • Develop models of TIGD3-DNA interactions based on structural predictions

  • Identify potential small molecule binding pockets through in silico screening

  • Use machine learning to predict TIGD3 binding patterns across the genome

• Cancer Biology + Immunology:

  • Investigate whether TIGD3 expression affects tumor immunogenicity

  • Explore TIGD3's potential role in immune cell function

  • Assess whether TIGD3-targeting approaches could enhance immunotherapy

• Developmental Biology + Epigenetics:

  • Map TIGD3 expression during development alongside epigenetic changes

  • Determine whether TIGD3 contributes to developmental programming

  • Investigate transgenerational effects of TIGD3 dysregulation

• Systems Biology + Network Science:

  • Position TIGD3 within larger gene regulatory networks

  • Identify network motifs and feedback loops involving TIGD3

  • Model the system-wide effects of TIGD3 perturbation

• Bioengineering + Molecular Biology:

  • Develop novel tools for visualizing TIGD3-DNA interactions

  • Create engineered TIGD3 variants with modified functions

  • Design synthetic biology approaches to exploit TIGD3 properties

• Clinical Research + Basic Science:

  • Correlate TIGD3 expression with detailed clinical phenotypes

  • Develop patient-derived models for studying TIGD3 function

  • Translate basic TIGD3 findings into potential biomarker applications

• Pharmacology + Structural Biology:

  • Design small molecules targeting TIGD3 or its interactions

  • Develop proteolysis-targeting chimeras (PROTACs) for TIGD3 degradation

  • Identify natural products that modulate TIGD3 function