DARS Antibody

What is DARS and what is its biological significance?

DARS (aspartyl-tRNA synthetase) is an essential enzyme responsible for catalyzing the attachment of aspartic acid to its cognate tRNA molecules during protein synthesis. This aminoacyl-tRNA synthetase plays a critical role in maintaining translational fidelity by ensuring the correct amino acid is incorporated into growing polypeptide chains. Recent research has revealed that DARS expression is dysregulated in various cancers, including renal cell carcinoma, glioblastoma, colon cancer, and gastric cancer . The protein's importance extends beyond its canonical role in translation, as emerging evidence suggests it may be involved in immune regulation and cellular signaling pathways that influence disease progression and therapy response.

The biological significance of DARS has been particularly highlighted in hematological disorders, where recent studies have demonstrated its overexpression in all subtypes of BCR/ABL1-negative myeloproliferative neoplasms (MPNs), including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF) . This overexpression has been correlated with specific clinical features such as splenomegaly, suggesting DARS may contribute to disease pathophysiology through mechanisms beyond its role in protein synthesis. The growing understanding of DARS's multifaceted functions underscores its potential as a biomarker and therapeutic target in various disease contexts.

What are the optimal methods for detecting and quantifying DARS expression?

Detecting and quantifying DARS expression in research settings requires choosing appropriate methodologies based on the specific research questions and available samples. Immunohistochemistry (IHC) represents one of the most widely employed techniques for evaluating DARS protein expression in tissue samples, as evidenced by its successful application in studies examining DARS in myeloproliferative neoplasms . When performing IHC, researchers should consider using validated anti-DARS antibodies with demonstrated specificity for human DARS protein, such as rabbit polyclonal antibodies that have undergone rigorous validation procedures . Proper tissue processing, antigen retrieval optimization, and appropriate controls are essential for generating reliable and reproducible IHC results.

For quantitative analysis of DARS expression at the protein level, Western blotting provides a complementary approach to IHC. This technique allows researchers to determine relative protein abundance across different samples and can be particularly valuable for comparing DARS expression between diseased and healthy tissues. At the transcriptional level, quantitative real-time PCR (qRT-PCR) and RNA sequencing represent powerful tools for measuring DARS mRNA expression. Flow cytometry offers another dimension for DARS analysis, particularly when examining expression patterns in specific cell populations within heterogeneous samples. This approach can be especially valuable when investigating correlations between DARS expression and immune cell composition, as observed in studies of myeloproliferative neoplasms where significant relationships between DARS expression and CD4+ T cells have been documented .

How should researchers select appropriate anti-DARS antibodies for their studies?

Selecting the optimal anti-DARS antibody is a critical decision that can significantly impact research outcomes. Researchers should first consider the intended application, as different experimental techniques (IHC, Western blot, immunofluorescence, etc.) may require antibodies with specific characteristics. For immunohistochemical applications, researchers should choose antibodies that have been validated specifically for IHC, as demonstrated by clear staining patterns that correspond to expected DARS localization . When working with human samples, it is essential to select antibodies with confirmed reactivity against human DARS protein, while ensuring minimal cross-reactivity with other proteins to avoid false-positive results.

How is DARS expression implicated in the pathogenesis of myeloproliferative neoplasms?

Recent research has revealed significant overexpression of DARS in BCR/ABL1-negative myeloproliferative neoplasms (MPNs), including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF) . This overexpression occurs at the protein level as determined through immunohistochemical analysis, suggesting a potential role for DARS in the molecular pathogenesis of these disorders. The mechanism by which DARS contributes to MPN development and progression likely extends beyond its canonical function in protein synthesis, possibly involving dysregulation of cellular signaling pathways or altered interactions with other molecular players in hematopoietic stem and progenitor cells, which are the primary cellular origins of MPNs.

Notably, elevated DARS expression has been linked to specific clinical manifestations in MPN patients, particularly splenomegaly . This association suggests that DARS may contribute to the extramedullary hematopoiesis or altered splenic function characteristic of advanced MPNs. The finding that DARS expression correlates negatively with CD4+ T cells (R = -0.451, P = 0.0004) and CD4+/CD8+ T cell ratio (R = -0.3758, P = 0.0040), while showing positive correlation with CD68+ tumor-associated macrophages (R = 0.4037, P = 0.0017), points to potential interactions between DARS and the immune microenvironment in MPN pathogenesis . These correlations suggest that DARS may influence the composition and function of immune cells within the bone marrow niche, potentially contributing to immune dysregulation observed in MPN patients.

Furthermore, the positive correlations between DARS expression and several cytokines, including IL-2 (R = 0.5419, P < 0.001), IL-5 (R = 0.3161, P = 0.0166), IL-6 (R = 0.2992, P = 0.0238), and IFN-γ (R = 0.3873, P = 0.0029), indicate a potential role for DARS in modulating inflammatory responses in the MPN microenvironment . IL-6, in particular, is known to play a critical role in MPN pathogenesis through JAK-STAT pathway activation, suggesting that DARS may intersect with this key signaling cascade. These findings collectively suggest that DARS may serve as both a biomarker and a functional contributor to MPN biology, possibly representing a novel therapeutic target in these disorders.

What is the relationship between DARS expression and immune cell composition in disease states?

The relationship between DARS expression and immune cell composition represents a fascinating area of research with important implications for understanding disease pathogenesis and developing targeted therapies. In myeloproliferative neoplasms (MPNs), studies have demonstrated significant correlations between DARS expression and specific immune cell populations . DARS expression shows a negative correlation with CD4+ T cells (R = -0.451, P = 0.0004) and CD4+/CD8+ T cell ratio (R = -0.3758, P = 0.0040), suggesting that elevated DARS may be associated with reduced helper T cell presence or function in the bone marrow microenvironment . This relationship could contribute to the immune dysregulation frequently observed in MPN patients and may influence disease progression and response to therapy.

Conversely, DARS expression positively correlates with CD68+ tumor-associated macrophages (R = 0.4037, P = 0.0017), indicating a potential interaction between DARS and macrophage recruitment or polarization in the disease microenvironment . Macrophages are known to influence the bone marrow niche in hematological malignancies and can promote disease progression through various mechanisms, including cytokine production and extracellular matrix remodeling. The association between DARS and macrophage presence suggests that DARS may contribute to creating a pro-tumorigenic microenvironment in MPNs, potentially through direct or indirect effects on macrophage function or recruitment.

The relationship between DARS expression and cytokine profiles provides further insight into its potential role in immune modulation. Positive correlations have been observed between DARS expression and several key cytokines, including IL-2, IL-5, IL-6, and IFN-γ . These cytokines play diverse roles in immune regulation, with IL-6 being particularly relevant to MPN pathogenesis through its activation of JAK-STAT signaling. The association between DARS and these cytokines suggests that DARS may influence the inflammatory milieu within the bone marrow, potentially creating conditions that support the propagation of malignant clones while suppressing normal hematopoiesis and immune surveillance. Understanding these relationships could inform the development of targeted therapeutic approaches that address both the malignant clone and the dysregulated immune microenvironment in MPNs and potentially other DARS-associated disorders.

How can researchers effectively analyze DARS expression data in correlation with clinical outcomes?

Effective analysis of DARS expression data in relation to clinical outcomes requires a multifaceted approach that integrates molecular findings with patient characteristics and disease parameters. When designing such studies, researchers should first establish clear criteria for quantifying DARS expression, whether through immunohistochemical scoring, protein quantification by Western blot, or mRNA expression analysis. Standardized scoring systems should be employed to minimize inter-observer variability and enable meaningful comparisons across different patient cohorts. For immunohistochemical analysis, both the intensity of staining and the percentage of positive cells should be considered, potentially using established methods such as H-score or Allred scoring systems.

Multivariate analyses are particularly important for determining whether DARS expression provides independent prognostic information beyond established clinical and molecular risk factors. Researchers should include relevant covariates in their models, such as age, disease stage, and the presence of known driver mutations (e.g., JAK2, CALR, or MPL mutations in myeloproliferative neoplasms). Additionally, stratification analyses based on disease subtypes or risk categories can reveal whether the clinical significance of DARS expression varies across different patient populations. To enhance the robustness and translational relevance of findings, researchers should consider validating their results in independent patient cohorts and correlating DARS expression with response to specific therapeutic interventions. This comprehensive analytical approach can provide valuable insights into the clinical utility of DARS as a biomarker and potential therapeutic target across various disease contexts.

What is the DARS (Decoys as the Reference State) approach in antibody-protein docking?

The DARS (Decoys as the Reference State) approach represents an innovative computational methodology designed to improve the accuracy of protein-protein docking, particularly in the context of antibody-antigen interactions. This knowledge-based approach develops interaction potentials by analyzing the statistics of atom-atom contacts in native protein complexes while using decoy structures as a reference state . The fundamental principle behind DARS is that the frequency of specific atom-atom contacts in native complexes, when compared to their frequency in incorrectly docked decoys, provides valuable information about the energetic favorability of these interactions. By incorporating this statistical knowledge into scoring functions, DARS helps distinguish native-like binding orientations from incorrect ones during the docking process.

The implementation of DARS in protein docking begins with the selection of a diverse set of protein-protein complexes with known structures to serve as the training set. From these structures, the frequency of contacts between different atom types at the protein-protein interface is calculated. Simultaneously, a large set of decoy structures (incorrect docking poses) is generated using shape complementarity as the primary criterion . The frequencies of atom-atom contacts in these decoys serve as the reference state, representing the expected distribution of contacts if proteins were to interact randomly. The final DARS potential is derived by taking the logarithm of the ratio between the frequency of a particular contact in native structures and its frequency in the decoy ensemble. This approach effectively captures the preferences and avoidances of different atom types for interacting with each other in native protein-protein interfaces.

While the original DARS potential demonstrated success in general protein-protein docking scenarios, researchers observed that it performed less optimally for antibody-protein antigen complexes . This limitation stemmed from a fundamental characteristic of the training data used for the original DARS, which was enriched in homodimers and enzyme-inhibitor complexes with symmetric interfaces. Antibody-antigen interfaces, in contrast, exhibit inherent asymmetry, with specific amino acids like phenylalanine, tryptophan, and tyrosine being highly represented in the antibody paratope but not in the antigen epitope. This recognition of the unique characteristics of antibody-antigen interfaces led to the development of specialized variants of DARS specifically tailored for antibody-protein docking applications.

How does the asymmetric DARS (aADARS) improve antibody-protein docking performance?

The aADARS approach overcomes this limitation by removing the symmetry constraint from the interaction potential. In this modified framework, the same atom type is treated differently depending on whether it is located on the antibody or the antigen . For example, a carbon atom in an aromatic residue on the antibody is considered distinct from a chemically identical carbon atom in an aromatic residue on the antigen. This distinction allows the potential to capture the unique interaction preferences that arise from the specialized nature of antibody-antigen recognition. The training process for aADARS utilizes a carefully curated set of non-redundant antibody-protein complexes, ensuring that the derived potentials accurately reflect the specific characteristics of these interactions rather than being influenced by other types of protein-protein interfaces that might have different binding properties.

Empirical testing of the aADARS potential has demonstrated its superior performance compared to both the original DARS and the symmetric antibody-antigen-specific DARS (aDARS) . In a benchmark study using 20 antibody-antigen complexes, aADARS successfully identified near-native docking poses in almost all cases, whereas other potentials failed for multiple complexes. Remarkably, this improved performance was achieved without incorporating any sequence information about the complementarity-determining regions (CDRs) of the antibody, indicating that the asymmetric potential inherently captures key aspects of antibody-antigen recognition. When the aADARS potential was combined with CDR masking (restricting potential binding sites to the antibody's CDRs), the docking performance improved even further. These results highlight the importance of accounting for the inherent asymmetry of antibody-antigen interfaces in computational docking approaches and suggest that aADARS can serve as a valuable tool for antibody engineering and epitope prediction applications.

What computational approaches can optimize antibody design targeting DARS proteins?

Optimizing antibody design for targeting DARS proteins requires integrating multiple computational approaches throughout the discovery and development pipeline. In silico antibody design protocols, such as IsAb, provide systematic frameworks for generating and refining antibodies with desired binding properties . These protocols typically begin with structure prediction of the target DARS protein if experimental structures are unavailable. Homology modeling or ab initio structure prediction can be employed to generate reliable three-dimensional models of DARS, which then serve as the foundation for subsequent design steps. For antibody structure prediction, specialized tools like RosettaAntibody can be utilized to construct models of the variable regions (Fv) based on the antibody sequence, employing template-based modeling for framework regions and complementarity-determining region (CDR) loops .

Following structure prediction, energy minimization using methods like RosettaRelax helps optimize the conformations of both the antibody and DARS protein models, bringing them closer to their likely bound states and increasing the accuracy of subsequent docking simulations . The docking process itself should employ a two-step approach, beginning with global docking to sample a wide range of potential binding orientations, followed by local docking to refine promising binding poses. For DARS-targeting antibodies, applying specialized docking methods that account for the unique characteristics of antibody-antigen interfaces is crucial. The asymmetric antibody-antigen-specific DARS (aADARS) potential, which removes the assumption of symmetry in interaction energetics, has demonstrated superior performance in predicting antibody-protein complexes compared to conventional docking potentials .

After generating potential antibody-DARS complexes, computational alanine scanning serves as a powerful tool for identifying key interaction residues (hotspots) that contribute significantly to binding affinity . By systematically mutating interface residues to alanine and calculating the resulting energy changes, researchers can identify the most critical residues for DARS recognition. These insights guide subsequent antibody optimization through computational affinity maturation protocols, which systematically explore mutations that enhance binding affinity while maintaining antibody stability . Machine learning approaches can further accelerate this process by predicting the effects of specific mutations based on training data from known antibody-antigen complexes. Throughout the design process, researchers should periodically validate computational predictions through experimental testing, creating an iterative cycle of design, testing, and refinement to develop antibodies with optimal specificity and affinity for DARS proteins.

How should researchers interpret conflicting data on DARS expression across different studies?

Interpreting conflicting data on DARS expression across different studies requires careful consideration of multiple methodological factors that may contribute to discrepancies. First, researchers should critically evaluate the techniques used for measuring DARS expression, as different methods (immunohistochemistry, Western blotting, qRT-PCR, etc.) may yield varying results due to differences in sensitivity, specificity, and the biomolecular aspect being measured (protein vs. mRNA). The specific antibodies or primers used in these assays can significantly impact results, with some antibodies potentially recognizing different isoforms or post-translationally modified forms of DARS. Standardization of detection methods, including consistent use of validated antibodies and well-established protocols, can help reduce methodological variability and facilitate more meaningful comparisons across studies.

When faced with conflicting data, researchers should consider employing meta-analytical approaches to systematically integrate findings across multiple studies. Such analyses can identify consistent patterns amid apparent discrepancies and may reveal important moderating factors that explain variations in DARS expression across different contexts. Additionally, collaborative efforts involving multicenter studies with standardized protocols can help overcome the limitations of individual studies and provide more robust insights into DARS expression patterns. Ultimately, addressing discrepancies requires not only recognizing potential sources of variation but also designing future studies with sufficient statistical power, appropriate controls, and comprehensive reporting of methodological details to facilitate replication and meta-analysis. Through such rigorous approaches, the field can develop a more coherent understanding of DARS expression and its implications for disease pathogenesis and therapeutic targeting.

What emerging technologies might advance our understanding of DARS function in disease?

Emerging technologies across multiple fields are poised to revolutionize our understanding of DARS function in disease contexts. Single-cell technologies represent one of the most promising approaches for unraveling the complexities of DARS expression and function with unprecedented resolution. Single-cell RNA sequencing (scRNA-seq) can reveal cell type-specific expression patterns of DARS within heterogeneous tissues, potentially identifying previously unrecognized cellular populations with distinctive DARS expression profiles. This approach is particularly valuable for studying diseases like myeloproliferative neoplasms, where malignant cells coexist with various normal cell types in the bone marrow microenvironment . Complementary techniques such as single-cell proteomics and single-cell ATAC-seq can provide insights into DARS protein levels and the epigenetic regulation of DARS expression at the individual cell level, creating a multi-dimensional understanding of DARS biology in health and disease.

CRISPR-Cas9 genome editing technologies offer powerful tools for investigating DARS function through precise genetic manipulation. CRISPR screening approaches, including both knockout and activation screens, can systematically evaluate the functional consequences of modulating DARS expression in disease-relevant cellular contexts. These screens may uncover synthetic lethal interactions and identify genes that become essential specifically in DARS-overexpressing cells, potentially revealing new therapeutic vulnerabilities. CRISPR base editing and prime editing technologies enable the introduction of specific DARS mutations identified in patient samples, allowing researchers to directly assess their functional impact and potential therapeutic targetability. Furthermore, CRISPR-mediated epigenome editing can help elucidate the mechanisms regulating DARS expression by manipulating specific epigenetic modifications at the DARS locus.

Advanced imaging technologies provide another dimension for studying DARS in disease contexts. Techniques such as multiplexed ion beam imaging (MIBI) and imaging mass cytometry (IMC) allow simultaneous visualization of multiple proteins, including DARS and associated signaling molecules, within the spatial context of tissue architecture. These approaches can reveal the spatial relationships between DARS-expressing cells and various immune cell populations, potentially providing insights into the mechanisms underlying the observed correlations between DARS expression and immune cell composition in diseases like myeloproliferative neoplasms . Additionally, live-cell imaging combined with fluorescent tagging of DARS can track its dynamics and subcellular localization in real-time, potentially uncovering non-canonical functions beyond its well-established role in protein synthesis. By integrating these diverse technological approaches, researchers can develop a comprehensive understanding of DARS biology in disease, potentially identifying new biomarkers and therapeutic strategies.

How can computational and experimental approaches be integrated to accelerate DARS-targeted antibody development?

Integrating computational and experimental approaches creates a powerful synergistic framework for accelerating the development of antibodies targeting DARS. The development pipeline can begin with in silico epitope prediction, employing machine learning algorithms trained on known antibody-antigen complexes to identify potentially immunogenic regions on the DARS protein. These predictions can be refined using molecular dynamics simulations to account for the conformational flexibility of DARS and identify epitopes that maintain accessibility across different protein states. Computational antibody design protocols, such as IsAb, can then generate candidate antibodies predicted to bind these epitopes with high affinity and specificity . The use of asymmetric antibody-antigen-specific DARS (aADARS) potentials in docking simulations can further enhance the accuracy of binding predictions, properly accounting for the unique characteristics of antibody-antigen interfaces .

These computational predictions must be validated and refined through targeted experimental approaches. High-throughput antibody display technologies, including phage, yeast, and mammalian display, enable the rapid screening of computationally designed antibody libraries against recombinant DARS protein or DARS-expressing cells. Surface plasmon resonance (SPR) and bio-layer interferometry (BLI) provide quantitative measurements of binding kinetics and affinities for promising antibody candidates, validating computational predictions and guiding further optimization. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) and X-ray crystallography offer complementary insights into the structural basis of antibody-DARS interactions, potentially revealing binding mechanisms not fully captured by computational models. These experimental data can then be fed back into computational pipelines to refine the models and improve the accuracy of future predictions.

This iterative integration creates a virtuous cycle where experimental validation informs computational refinement, and computational insights guide experimental design. For example, experimentally determined structures of antibody-DARS complexes can be used to train improved docking algorithms, while computational alanine scanning can identify key residues for targeted mutagenesis in affinity maturation experiments . Advanced data integration frameworks, potentially leveraging machine learning approaches, can systematically combine diverse computational predictions and experimental measurements to rank antibody candidates and prioritize those with the most promising properties for further development. Furthermore, the integration of computational and experimental approaches extends beyond initial discovery to optimization phases, where in silico predictions of antibody developability characteristics (stability, solubility, immunogenicity) can guide engineering efforts to enhance manufacturable properties while maintaining target binding. This holistic approach not only accelerates DARS-targeted antibody development but also increases the likelihood of identifying candidates with optimal combinations of specificity, affinity, and developability for therapeutic applications.