DDR1 Human

What is DDR1 and what are its primary biological functions?

DDR1 is a collagen-activated receptor tyrosine kinase (RTK) that plays pivotal roles in regulating cellular functions including proliferation, differentiation, invasion, migration, and matrix remodeling . It functions as a critical collagen receptor that mediates cell-matrix interactions and subsequent signal transduction. DDR1 activation triggers various downstream signaling pathways that regulate cellular behavior in both physiological and pathological contexts.

The biological functions of DDR1 extend beyond basic cellular processes. Research has demonstrated that DDR1 plays an important role in regulating attachment to collagen, chemotaxis, proliferation, and matrix metalloproteinase (MMP) production in smooth muscle cells . These functions highlight DDR1's significance in tissue homeostasis and remodeling processes.

How is DDR1 activated and what are its primary ligands?

DDR1 is primarily activated by various types of collagen, with collagen IV being a particularly important ligand that exclusively activates DDR1 . The activation process involves collagen binding to the discoidin domain of DDR1, which induces receptor dimerization and autophosphorylation of tyrosine residues in the intracellular domain, particularly at Tyr792 .

Activation protocols in experimental settings typically involve treating cells with purified collagen (commonly at concentrations of 5-20 μg/mL) for 60-90 minutes. For example, in studies with oligodendroglial human cell lines, researchers have used collagen IV at a concentration of 10 μg/mL to activate DDR1 . This activation can be specifically blocked using selective inhibitors such as DDR1-IN-1, which effectively prevents DDR1 autophosphorylation at concentrations of 1-5 μM .

What experimental methods are most reliable for detecting DDR1 expression and activation?

Several complementary approaches are recommended for reliably detecting DDR1 expression and activation:

For DDR1 expression analysis:

• qPCR using gene-specific Assays-on-Demand or custom TaqMan assays to detect total DDR1 and specific isoforms (DDR1a, DDR1b, DDR1c)

• Western blotting using antibodies targeting the C-terminal intracellular fragment of DDR1

• Immunohistochemistry for tissue localization studies

For DDR1 activation/phosphorylation detection:

• Western blotting with phospho-specific antibodies (e.g., anti-Tyr792 DDR1 antibody)

• ELISA-based phosphorylation assays

• Functional assays measuring downstream signaling events

For optimal results in Western blot analysis, researchers have successfully used conditions including separation on 8% Tris-glycine gels under reducing conditions, transfer to nitrocellulose membranes, blocking with 4% skim milk and 0.1% Tween-20 in TBS, and overnight incubation with primary antibodies at specified dilutions (e.g., 1:500 for anti-Tyr792 DDR1) .

How does DDR1 expression vary across cancer types and what are the implications for prognosis?

Pan-cancer analyses have revealed that DDR1 expression patterns vary significantly across different cancer types. Bioinformatic studies using data from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and Genotype Tissue-Expression (GTEx) databases have demonstrated that DDR1 is expressed at high levels in most cancers .

Importantly, the relationship between DDR1 expression and patient prognosis is cancer type-dependent. DDR1 expression can be either positively or negatively associated with survival outcomes depending on the specific cancer . This context-dependent prognostic value suggests that DDR1 may play distinct roles in different tumor microenvironments and cellular contexts.

Research methodologies for investigating these relationships typically involve:

What is the relationship between DDR1 expression and genomic/epigenomic features in cancer?

DDR1 expression in cancer shows significant associations with various genomic and epigenomic features:

DNA methylation: DDR1 expression is significantly associated with DNA methylation patterns in at least 8 cancer types . Researchers can investigate this relationship using tools available in the Gene Set Cancer Analysis (GSCA) database to evaluate the correlation between DDR1 mRNA expression and its DNA methylation status across different cancers .

Microsatellite instability (MSI): DDR1 expression shows significant correlation with MSI in 6 cancer types . This suggests potential interactions between DDR1 signaling and DNA repair mechanisms.

Tumor mutation burden (TMB): Significant associations between DDR1 expression and TMB have been identified in 11 cancer types , indicating possible relationships between DDR1 and genomic instability processes.

RNA methylation and mismatch repair genes: DDR1 expression correlates with RNA methylation-related genes and mismatch repair genes in most cancers , suggesting complex regulatory networks involving DDR1.

These findings highlight the importance of integrating multi-omics approaches when studying DDR1 in cancer, combining expression analysis with methylation profiling, mutation analysis, and other genomic features.

How does DDR1 influence the tumor microenvironment and immune response?

DDR1 plays a significant role in shaping the tumor microenvironment (TME) and modulating immune responses within tumors. Research has demonstrated that DDR1 expression is significantly correlated with immune cell infiltration in various cancers .

Using the ESTIMATE algorithm, researchers have calculated immune and stromal scores for tumor samples across 33 cancer types to evaluate the relationship between DDR1 expression and these TME components . This relationship can be analyzed using R packages such as "estimate" and "limma" to correlate DDR1 expression with the degree of immune infiltration.

DDR1 expression also shows significant correlations with various immune-related genes across different cancer types . This suggests that DDR1 may influence immune surveillance mechanisms and potentially impact immunotherapy responses.

Methodologically, researchers investigating these relationships should consider:

• Deconvolution analyses of bulk RNA-seq data to estimate immune cell proportions

• Single-cell RNA sequencing to precisely characterize DDR1-expressing cells and their interactions with immune populations

• Functional studies using co-culture systems or organoid models to validate computational findings

What are the current approaches for developing selective DDR1 inhibitors?

While no selective small-molecule DDR1 inhibitors have reached clinical trials to date, significant progress has been made in developing potent and selective compounds. Current approaches in DDR1 inhibitor development include:

• Structure-based drug design leveraging the unique structural characteristics of DDR1

• High-throughput screening of compound libraries

• Rational modification of existing tyrosine kinase inhibitors to enhance DDR1 selectivity

• Development of dual-target or multitarget inhibitors that modulate DDR1 along with complementary pathways

Several compounds have shown promise in preclinical studies. For example, BK40143 has demonstrated significant therapeutic potential for neurodegenerative diseases . DDR1-IN-1 has been established as a highly selective inhibitor of DDR1 autophosphorylation, effective at concentrations of 1-5 μM .

Understanding the structure-activity relationship of DDR1 inhibitors is crucial for advancing drug development efforts. Researchers should focus on:

• Characterizing the binding mode of inhibitors to DDR1

• Assessing selectivity profiles against other kinases

• Optimizing pharmacokinetic properties

• Evaluating efficacy in disease-relevant preclinical models

How can in vitro experimental models be optimized for studying DDR1 function?

Optimizing in vitro models for DDR1 research requires careful consideration of multiple factors:

Cell line selection:

• Human embryonic kidney fibroblast 293 cells have been successfully used for transient expression of DDR1

• HCT116 cells serve as a positive control in Western blot experiments due to their high DDR1 expression

• Disease-specific cell lines should be selected based on research focus (e.g., HOG16 cells for oligodendroglial studies )

Experimental conditions for DDR1 activation:

• Serum starvation (18-24 hours in FBS-free medium) to avoid the presence of collagen and other potential DDR1 ligands

• Stimulation with purified collagen (typically 10 μg/ml type I or type VIII collagen for 90 minutes)

• Use of selective inhibitors (e.g., DDR1-IN-1 at 5 μM) as experimental controls

Detection methods:

• Western blotting with phospho-specific antibodies (anti-Tyr792 DDR1)

• qPCR for isoform-specific expression analysis

• Functional assays measuring cellular outcomes (proliferation, migration, etc.)

For genetic manipulation studies, DDR1-null models have been developed. For example, DDR1-null mice have been generated by targeting the DDR1 gene through deletion of critical exons . These models provide valuable tools for studying DDR1 function in various physiological and pathological contexts.

What is the potential of DDR1 as a therapeutic target in fibrotic diseases?

Beyond cancer, DDR1 represents a promising therapeutic target for fibrotic diseases due to its role in collagen-mediated signaling and tissue remodeling. Animal studies have demonstrated that DDR1 plays an important role in collagen deposition following vascular injury. For instance, cross-sectional area of neointima was significantly lower in DDR1-null mice than in wild-type mice following mechanical injury to carotid arteries, with a significant decrease in collagen deposition in the injured arteries of DDR1-null mice .

Researchers investigating DDR1 in fibrosis should consider:

• Examining DDR1 expression in patient samples from various fibrotic disorders

• Using genetic knockout models to assess the impact of DDR1 deletion on fibrosis development

• Testing DDR1 inhibitors in established models of fibrosis (kidney, liver, lung, etc.)

• Investigating the molecular mechanisms by which DDR1 regulates extracellular matrix production and remodeling

The development of selective DDR1 inhibitors may offer new therapeutic options for fibrotic conditions with high unmet medical need.

How can single-cell approaches enhance our understanding of DDR1 biology?

Single-cell technologies offer powerful approaches to dissect the heterogeneous expression and function of DDR1 across different cell populations within complex tissues:

• Single-cell RNA sequencing (scRNA-seq) can reveal cell type-specific expression patterns of DDR1 and its correlation with distinct cellular states and gene expression programs

• Single-cell ATAC-seq can identify regulatory elements controlling DDR1 expression in specific cell populations

• Spatial transcriptomics can map DDR1 expression within tissue architecture, revealing relationships with the microenvironment

Researchers have begun to leverage single-cell sequencing databases to investigate the biological function of DDR1 in tumors . These approaches can identify previously unrecognized cell populations that express DDR1 and characterize their functional states.

For researchers pursuing single-cell analyses of DDR1, it is advisable to:

• Integrate multiple single-cell modalities when possible

• Validate findings using orthogonal methods (immunostaining, in situ hybridization)

• Perform trajectory analyses to understand DDR1's role in cell state transitions

• Correlate single-cell data with clinical outcomes in patient samples

What computational approaches are most effective for pan-cancer analysis of DDR1?

Pan-cancer analysis of DDR1 requires robust computational approaches to integrate and interpret data across multiple cancer types. Effective methodologies include:

• Data collection and preprocessing:

  • Accessing gene expression, somatic mutations, and clinical data from databases like UCSC Xena, GTEx, and CCLE

  • Applying appropriate normalization methods to account for batch effects and platform differences

• Correlation analyses:

  • Evaluating associations between DDR1 expression and clinical outcomes using Kaplan-Meier and univariate Cox regression analyses

  • Examining relationships with genomic features (methylation, MSI, TMB) using appropriate statistical methods

• Immune infiltration analysis:

  • Applying deconvolution algorithms to estimate immune cell proportions from bulk RNA-seq data

  • Using the ESTIMATE algorithm to calculate immune and stromal scores

• Pathway and network analyses:

  • Conducting enrichment analyses to identify biological processes associated with DDR1 expression

  • Constructing protein-protein interaction networks to contextualize DDR1 function

• Visualization and interpretation:

  • Developing informative visualizations (heatmaps, forest plots, etc.) to communicate complex relationships

  • Integrating findings across cancer types to identify common and distinct patterns

These computational approaches should be implemented using established R packages such as "survival," "forestplot," "estimate," and "limma" for robust and reproducible analyses .

How can DDR1 expression be effectively assessed in clinical samples?

Reliable assessment of DDR1 expression in clinical samples is critical for translational research and potential diagnostic applications. Recommended approaches include:

Tissue preparation and processing:

• Proper tissue preservation (fresh-frozen or formalin-fixed paraffin-embedded)

• Standardized protocols for RNA and protein extraction

• Quality control measures to ensure sample integrity

Detection methods:

• Immunohistochemistry (IHC): Using validated antibodies against DDR1 with appropriate positive and negative controls

• RT-qPCR: Employing well-designed primers and probes for DDR1 and its isoforms

• NanoString technology: For multiplexed gene expression analysis including DDR1 and related genes

• RNA-seq: For comprehensive transcriptomic profiling

Data analysis and interpretation:

• Standardized scoring systems for IHC (e.g., H-score, Allred score)

• Normalization to appropriate reference genes for qPCR (e.g., RPLP0 and β2M as used in published studies )

• Incorporation of relevant clinical and pathological parameters in analyses

Researchers should consider the limitations of each approach and potentially employ multiple methods for cross-validation in critical studies.

What is the potential of DDR1 as a biomarker for patient stratification in clinical trials?

DDR1 shows promise as a biomarker for patient stratification in clinical trials, particularly in oncology. This potential is supported by several observations:

• DDR1 expression varies across cancer types and correlates with prognosis in a cancer type-dependent manner

• DDR1 expression is associated with immune cell infiltration and tumor microenvironment characteristics

• DDR1 shows relationships with genomic features that may influence treatment response (MSI, TMB, etc.)

To effectively implement DDR1 as a stratification biomarker, researchers should:

• Define clear, reproducible cutoffs for DDR1 expression levels based on rigorous statistical analyses

• Validate the prognostic or predictive value of DDR1 in independent patient cohorts

• Develop standardized assays suitable for clinical implementation

• Investigate combinations of DDR1 with other biomarkers for improved stratification

The contextual nature of DDR1's significance across cancer types highlights the importance of cancer-specific validation before clinical implementation.

What are the key considerations for studying DDR1 isoforms?

DDR1 exists in multiple isoforms (including DDR1a, DDR1b, and DDR1c) with potentially distinct functional properties. Key considerations for studying these isoforms include:

Isoform-specific detection:

• Design of isoform-specific primers and probes for qPCR analysis

• Custom TaqMan assays can be employed to detect the expression of specific DDR1 isoforms

• Selection of antibodies that can distinguish between isoforms when possible

Expression system selection:

• Careful choice of expression vectors for overexpression studies

• Consideration of endogenous isoform expression in selected cell lines

• Potential use of isoform-specific knockdown or knockout approaches

Functional characterization:

• Comparative analysis of isoform-specific effects on downstream signaling

• Assessment of differential ligand binding or activation kinetics

• Evaluation of isoform-specific protein-protein interactions

Data interpretation:

• Consideration of tissue-specific isoform expression patterns

• Analysis of isoform ratios rather than absolute expression levels

• Integration of isoform data with functional outcomes

Understanding isoform-specific functions may reveal more targeted therapeutic approaches and explain context-dependent effects of DDR1 in different tissues and disease states.

How can researchers address the challenge of studying DDR1 in complex in vivo environments?

Studying DDR1 in complex in vivo environments presents several challenges that can be addressed through strategic approaches:

Animal models:

• Use of DDR1-null mice generated through targeted deletion of critical exons in the DDR1 gene

• Development of conditional knockout models for tissue-specific DDR1 deletion

• Generation of humanized models expressing human DDR1 variants

Disease models:

• Established models of vascular injury to study DDR1's role in neointimal thickening

• Cancer xenograft models to investigate DDR1 in tumor progression

• Models of fibrotic diseases to examine DDR1's role in pathological matrix remodeling

Advanced imaging approaches:

• Intravital microscopy to visualize DDR1-expressing cells in their native environment

• PET imaging with radiolabeled DDR1-targeted agents

• Optical imaging using fluorescently labeled antibodies or ligands

Multi-parameter analysis:

• Integration of in vivo phenotypic data with ex vivo molecular analyses

• Single-cell analyses of tissues from animal models

• Correlation of animal model findings with human patient data

These approaches can provide more physiologically relevant insights into DDR1 function while addressing the limitations of simpler in vitro systems.