Recombinant Human Probable G-protein coupled receptor 142 (GPR142)

What is GPR142 and what is its primary physiological role?

GPR142 is a G protein-coupled receptor that functions primarily as a tryptophan-sensing receptor with significant expression in pancreatic β-cells. It plays a crucial role in glucose homeostasis by enhancing glucose-dependent insulin secretion when activated by tryptophan and other agonists. The receptor has been implicated in both metabolic regulation and inflammatory processes, suggesting multifaceted physiological roles. The receptor's activation initiates intracellular signal transduction that ultimately leads to enhanced glucose-dependent insulin secretion in isolated mouse islets, making it a potential therapeutic target for type 2 diabetes treatment . Recent research also suggests important roles in inflammatory pathways, as GPR142 expression is significantly modulated by proinflammatory cytokines .

How is GPR142 expression regulated in different tissues?

GPR142 expression is subject to dynamic regulation, particularly by inflammatory mediators. Research has demonstrated that proinflammatory cytokines, including TNF-α, IL-6, and IL-1β, directly upregulate GPR142 mRNA expression in ghrelin-producing cells (MGN3-1 cell line) . This regulation has been confirmed in animal models, where lipopolysaccharide (LPS) injection significantly increased GPR142 expression in the mouse stomach . In human samples, GPR142 mRNA expression levels in stomach tissue from morbidly obese patients showed positive correlations with TNF-α, IL-6, and IL-1β mRNA levels, further supporting cytokine-mediated regulation . This inflammatory regulation suggests GPR142 may serve as a link between inflammatory processes and metabolic function.

What cellular models are most appropriate for studying GPR142 function?

When designing experiments to study GPR142, researchers should carefully select cellular models based on their specific research questions:

When studying insulin secretion specifically, primary pancreatic islets represent the gold standard model, as they maintain the glucose-dependency of GPR142-stimulated insulin secretion that is observed in physiological settings .

How can researchers effectively model the 3D structure of GPR142?

In the absence of experimentally determined crystal structures, computational modeling approaches provide valuable insights into GPR142 structure. A systematic approach to modeling GPR142 structure involves:

• Sequence retrieval from databases like UniProt (ID: Q7Z601) or GenBank (ID: NP-861455.1) .

• Application of threading and ab initio methods, particularly when sequence homology with known structures is low (GPR142 shows only 21% homology with available structures) .

• Template selection: While challenging due to low homology, delta-type opioid receptor chimeric protein (PDB ID: 4N6H) has been used as an initial reference template .

• Model building using software like Modeler v9.8, followed by refinement and optimization .

• Validation using multiple assessment tools available through services like SAVE server, examining parameters such as Ramachandran plot statistics (with successful models showing >90% residues in allowed regions) .

• Domain prediction using specialized tools like TMbase and GPCRHMM server to properly characterize transmembrane regions .

• Binding site prediction using energy-based methods like SiteMap to identify potential ligand interaction sites .

This modeling approach has successfully predicted functional domains and binding sites in GPR142, enabling further structure-based virtual screening for potential agonists .

What methods are recommended for investigating GPR142-mediated signaling?

To effectively characterize GPR142 signaling pathways, researchers should employ multiple complementary approaches:

• G-protein coupling assays: To determine which G-protein subtypes (Gq, Gi, Gs, G12/13) couple to GPR142 upon activation. This typically involves measuring second messengers (calcium, cAMP) or using BRET/FRET-based proximity assays .

• Pathway inhibitor studies: Utilizing selective inhibitors of different G-protein pathways (e.g., YM-254890 for Gq/11 inhibition) to determine which pathway is necessary for specific functional outcomes .

• Comparative analysis of signaling in different cellular contexts: Studies should compare signaling in recombinant systems versus primary cells, as GPR142 shows context-dependent signaling preferences (Gq and Gi in HEK293 cells, but primarily Gq in primary islets) .

• Systems biology approaches: Construction of mathematical computational models of signaling pathways using software like Cell Designer v4.4, followed by kinetic simulations to understand interactions between components and their effects on insulin secretion .

These methodological approaches help delineate the complex signaling mechanisms of GPR142 and their downstream effects on cellular functions such as insulin secretion.

How does inflammation impact GPR142 expression and function?

The relationship between inflammation and GPR142 represents a significant area of investigation with several key findings:

• Direct cytokine regulation: Proinflammatory cytokines (TNF-α, IL-6, IL-1β) directly increase GPR142 mRNA expression in ghrelin-producing MGN3-1 cells, establishing a clear molecular link between inflammation and GPR142 expression .

• In vivo confirmation: LPS injection in mice, which induces systemic inflammation, significantly increases GPR142 expression in stomach tissue, validating the in vitro findings in a physiologically relevant model .

• Clinical correlation: In human stomach samples from morbidly obese patients, GPR142 mRNA levels positively correlate with proinflammatory cytokine (TNF-α, IL-6, IL-1β) expression, suggesting this regulatory relationship exists in human pathophysiology .

• Potential feedback loops: Given that GPR142 itself has been implicated in the regulation of inflammation, there may be complex regulatory feedback mechanisms between GPR142 activity and inflammatory processes that warrant further investigation .

This inflammation-GPR142 axis may have important implications for understanding metabolic disorders characterized by chronic low-grade inflammation, such as obesity and type 2 diabetes, potentially revealing new therapeutic avenues.

What structure-function relationships have been identified in GPR142?

Structure-function analysis of GPR142 has revealed several important features despite the challenges in characterizing this receptor:

• Binding pocket characteristics: Computational modeling has identified potential binding sites and tunnel regions in GPR142 that are crucial for ligand interaction. These sites have specific hydrophobic, polar, and charged properties that influence ligand binding .

• Transmembrane domains: Domain prediction algorithms have identified the transmembrane regions of GPR142, which are critical for its structural integrity and function as a GPCR .

• Ligand interaction sites: Molecular dynamics simulations with potential agonists like compound2 and compound21 have helped characterize key residues involved in ligand binding and receptor activation .

• Signal transduction elements: The receptor's coupling to different G-proteins (Gq vs. Gi) appears to be determined by structural elements that show context-dependent activation, suggesting conformational flexibility in the receptor .

• Structure-based virtual screening: Using the predicted 3D structure, researchers have identified novel potential agonists through structure-based virtual screening, validating the utility of these structural models for drug discovery efforts .

These structure-function insights provide a foundation for rational design of GPR142-targeted compounds and further understanding of its activation mechanisms.

What are the differences in GPR142 signaling between recombinant systems and primary cells?

A critical aspect of GPR142 research is understanding how its signaling differs between experimental systems:

What approaches have been successful in identifying novel GPR142 agonists?

Several complementary approaches have proven effective in identifying novel GPR142 agonists:

• Structure-based virtual screening: Using predicted 3D models of GPR142, researchers have screened large compound libraries (>1 million compounds) through molecular docking to identify potential agonists .

• Pharmacophore modeling: Based on known active GPR142 agonists with varying EC50 values (0.036-33.00), pharmacophore hypotheses have been generated to identify structural features critical for receptor binding and activation .

• Molecular dynamics simulations: MD simulations of GPR142 in complex with potential agonists (e.g., 50 ns simulations) help evaluate binding stability and conformational changes associated with receptor activation .

• Induced-fit docking studies: This approach accounts for receptor flexibility upon ligand binding, providing more accurate predictions of binding modes compared to rigid docking .

• Biochemical pathway analysis: Constructing pathway models that integrate GPR142 signaling with downstream effects helps predict compounds' efficacy in modulating insulin secretion .

This multi-faceted approach to GPR142 agonist discovery has led to identification of promising compounds that could serve as starting points for therapeutic development targeting metabolic disorders.

How can GPR142 activity be effectively measured in research contexts?

Reliable measurement of GPR142 activity is crucial for characterizing receptor function and evaluating potential agonists:

When designing experiments to assess GPR142 activity, researchers should select methods appropriate to their specific research questions, ideally combining multiple approaches to build a comprehensive understanding of receptor function.

What is the current understanding of tryptophan-GPR142 interactions?

As the endogenous ligand for GPR142, tryptophan interacts with this receptor in several important ways:

• Binding specificity: GPR142 functions as a tryptophan-sensing receptor, with the amino acid tryptophan serving as its native ligand .

• Signal transduction: Tryptophan binding to GPR142 initiates intracellular signal transduction that ultimately leads to enhanced glucose-dependent insulin secretion in pancreatic β-cells .

• Physiological relevance: This tryptophan sensing mechanism provides a direct link between amino acid availability and insulin secretion, contributing to metabolic regulation after protein-rich meals .

• Synthetic mimetics: Based on understanding the tryptophan-GPR142 interaction, researchers have developed synthetic agonists that either mimic tryptophan structure or identify novel chemotypes that activate the receptor through similar binding modes .

• Potential for allosteric modulation: Beyond the orthosteric tryptophan binding site, research has explored potential allosteric binding sites that could modify receptor response to tryptophan .

This fundamental understanding of tryptophan-GPR142 interactions provides the foundation for development of more potent and selective synthetic agonists with potential therapeutic applications.

How might GPR142 serve as a therapeutic target for metabolic disorders?

GPR142 holds significant promise as a therapeutic target for metabolic disorders, particularly type 2 diabetes:

• Glucose-dependent insulin secretion: GPR142 agonists stimulate insulin secretion only under elevated glucose conditions, suggesting they may promote normoglycemia without risk of hypoglycemia, a significant advantage over some current diabetes therapies .

• Novel mechanism of action: As a distinct target from currently approved therapies, GPR142 modulators could offer complementary approaches for patients inadequately controlled on existing medications .

• Potential for combination therapy: Given its specific mechanism of action, GPR142 agonists might synergize with existing diabetes treatments that work through different pathways .

• Inflammation-metabolism interface: The regulation of GPR142 by inflammatory cytokines suggests targeting this receptor might address aspects of metabolic inflammation associated with obesity and diabetes .

• Challenge of selectivity: Developing compounds with high specificity for GPR142 over other GPCRs remains a significant challenge that must be addressed to minimize off-target effects .

Future research should focus on optimizing lead compounds identified through virtual screening, evaluating their efficacy and safety in preclinical models, and exploring potential combination approaches with existing diabetes therapies.

What are the most pressing unanswered questions regarding GPR142 biology?

Despite significant progress, several key questions about GPR142 remain to be addressed:

• Tissue-specific functions: While GPR142's role in pancreatic β-cells is relatively well-characterized, its functions in other tissues where it is expressed remain largely unknown and warrant investigation .

• Physiological regulation: Beyond inflammatory regulation, other physiological factors that modulate GPR142 expression and activity under normal and pathological conditions need further characterization .

• Signaling pathway integration: How GPR142 signaling integrates with other metabolic signaling pathways, particularly in complex disorders like diabetes and obesity, requires more comprehensive study .

• Structural determinants of function: Although computational models provide insights, experimental structural data (e.g., cryo-EM or crystal structures) would significantly advance understanding of GPR142 activation mechanisms .

• Chronic activation effects: The long-term consequences of GPR142 activation on β-cell function, particularly whether it leads to adaptive changes or desensitization, remain poorly understood .

Addressing these questions will require integration of advanced techniques in structural biology, systems pharmacology, and physiological studies in relevant disease models to fully elucidate GPR142 biology and therapeutic potential.

How can computational models enhance understanding of GPR142 pathways?

Computational modeling represents a powerful approach to advance GPR142 research in several key areas:

• Comprehensive pathway modeling: Mathematical models integrating GPR142 with downstream signaling networks can predict system-wide effects of receptor activation on insulin secretion and identify critical nodes for intervention .

• Drug response prediction: Kinetic simulations of GPR142 pathways can predict responses to compounds with different potencies and at varying concentrations, informing optimal dosing strategies .

• Virtual clinical trials: Advanced models could simulate receptor response in various patient populations, helping prioritize candidates for clinical development .

• Integration of multi-omics data: Computational frameworks can integrate transcriptomic, proteomic, and metabolomic data to understand GPR142's role in complex biological networks .

• Artificial intelligence approaches: Machine learning algorithms trained on existing GPR142 agonist data could identify novel chemotypes with improved properties for therapeutic development .