Adhesion G Protein-Coupled Receptor G5

Introduction

Background and Biological Basis

ADGRG5 (Adhesion G protein-Coupled Receptor G5), also known as GPR112, is a member of the adhesion G protein-coupled receptor (aGPCR) family. These receptors are a unique class of G protein-coupled receptors characterized by a large extracellular domain that facilitates cell-cell and cell-matrix interactions. Like other GPCRs, ADGRG5 plays a crucial role in transducing extracellular signals into intracellular responses, influencing a wide range of cellular processes. ADGRG5has been identified in various cell types, including vascular smooth muscle cells, renal mesangial cells, and platelets. Its presence in platelets suggests an important involvement in platelet biology, a key aspect of the body’s hemostatic system.^[1]

Clinical Relevance

Genetic variations within the ADGRG5gene have been investigated for their potential impact on human health. Specifically, the single nucleotide polymorphism (SNP)rs10514919 , located in or near the ADGRG5 gene, has been associated with levels of platelet aggregation. Studies have shown a link between rs10514919 and ADP-induced, collagen-induced, and epinephrine-induced platelet aggregation. ^[1] Platelet aggregation is a vital process for forming blood clots to stop bleeding. However, abnormal platelet function can lead to health issues, ranging from excessive bleeding to the formation of dangerous clots (thrombosis) that contribute to conditions like heart attacks and strokes. Therefore, variations in ADGRG5that influence platelet activity may affect an individual’s susceptibility to these cardiovascular and hemostatic disorders.

Social Importance

Understanding the genetic underpinnings of platelet function, such as the role of ADGRG5, holds significant social importance due to the widespread impact of cardiovascular diseases. These diseases, often involving thrombotic events, are major global health challenges. Identifying genetic variants like those inADGRG5 that modulate platelet aggregation can contribute to personalized medicine, allowing for more targeted risk assessment and potentially guiding therapeutic strategies. For instance, this knowledge could help in tailoring antiplatelet therapies to individual genetic profiles, aiming to improve treatment efficacy and reduce adverse drug reactions. Furthermore, continued research into ADGRG5enhances our overall understanding of complex biological pathways and disease mechanisms, which could lead to the development of novel diagnostic tools and therapeutic interventions.

Limitations

Study Design and Statistical Power

Genome-wide association studies (GWAS) often face inherent limitations related to study design and statistical power, which can impact the interpretation of genetic associations with traits such as ADGRG5. Many studies acknowledge having limited statistical power to detect genetic effects of modest size, especially when accounting for the extensive multiple testing required across millions of single nucleotide polymorphisms (SNPs).^[2] While some research involves thousands of participants ^[3] the specific number of individuals with complete phenotype data for any given trait can be considerably smaller, further reducing the ability to identify weaker genetic associations. ^[4] This constraint means that genuine genetic influences with small effect sizes may remain statistically undetected, leading to an incomplete understanding of the trait’s genetic architecture.

The rigorous statistical thresholds necessary to correct for multiple comparisons in GWAS can also pose a challenge, as the absence of genome-wide significance does not definitively exclude a genetic role for a variant. ^[2] Furthermore, the reliance on imputation based on reference panels, such as HapMap, means that variants not well-represented in these panels or those with low imputation quality may be missed or inaccurately assessed. ^[5] The coverage of SNP arrays, particularly earlier generations, can be insufficient to comprehensively capture all genetic variation within a gene region, potentially overlooking real associations and necessitating denser arrays or targeted genotyping for a more complete picture. ^[4] Consequently, findings often require validation through replication in independent cohorts to confirm their authenticity and prevent false-positive conclusions, which is a fundamental challenge in prioritizing SNPs for follow-up. ^[6]

Generalizability and Phenotype Heterogeneity

The generalizability of genetic findings is often constrained by the demographic characteristics of the study populations. Many association studies primarily focus on populations of specific ancestries, such as self-identified Caucasians. ^[3] While sophisticated methods like principal component analysis and genomic control are applied to correct for population stratification within these groups ^[7] the findings may not be directly applicable or equally relevant to diverse ethnic groups. This limited ancestral representation restricts the broad applicability of identified genetic associations, highlighting the ongoing need for studies across multiple ancestral backgrounds to fully understand the genetic influences on complex traits globally.

Moreover, the precise definition and measurement of complex phenotypes can introduce variability that impacts the detection and interpretation of genetic associations. Factors such as the specific assays used, the timing of measurements, and the adjustments made for covariates like age and sex can influence the observed associations. ^[8] It is possible that the effects of some genetic loci are mediated through these covariates, and different analytical approaches or phenotype adjustments could yield varying results, making direct comparisons challenging and potentially obscuring true genetic effects. Such heterogeneity in phenotype ascertainment can lead to inconsistent findings across studies, complicating efforts to synthesize results and draw definitive conclusions about genetic mechanisms.

Functional Understanding and Translational Gaps

While genome-wide association studies are successful in identifying statistical links between genetic variants and traits, they often do not immediately reveal the underlying biological mechanisms. ^[6] Many associated SNPs may not reside within known genes or have clear functional implications, posing a significant challenge for understanding how they contribute to the observed phenotype. ^[1] Therefore, comprehensive functional analyses are essential to move beyond statistical association and elucidate how these genetic variants operate at a molecular and cellular level, which is crucial for translating genetic discoveries into clinical insights or therapeutic targets.

The current understanding of the genetic architecture of complex traits remains incomplete, with identified variants often explaining only a fraction of the total phenotypic variance. This implies that a substantial portion of the genetic influences, potentially including rare variants, structural variations, or complex gene-gene and gene-environment interactions, remains to be discovered and characterized. Fully dissecting the roles of individual SNPs and haplotypes requires additional genotyping and detailed analyses beyond the scope of initial GWAS, highlighting an ongoing need for deeper investigation and the integration of diverse ‘omics data to build a more complete biological picture. ^[1]

Variants

The complement factor H (CFH) gene plays a crucial role in regulating the complement system, a vital part of the innate immune response that helps protect the body from pathogens and clear cellular debris. CFH acts as a key negative regulator, preventing excessive activation of the alternative complement pathway on host cell surfaces, thereby distinguishing self from non-self and preventing autoimmune damage. ^[9] Variations within CFH, such as rs201263987 , can influence the efficiency of this regulation, potentially leading to increased inflammation or compromised immune function. Such dysregulation can have widespread implications for cellular health and tissue integrity, which are fundamental to processes mediated by adhesion G protein-coupled receptors (ADGRs), including ADGRG5, which are involved in cell-cell and cell-matrix interactions and play roles in immune modulation and tissue homeostasis. ^[10]

Several other variants are implicated in inflammatory and immune responses, which often overlap with the broad functions of adhesion G protein-coupled receptors. For instance, single nucleotide polymorphisms (SNPs) likers2494250 and rs4128725 are associated with monocyte chemoattractant protein-1 (MCP1) concentrations, a key mediator of inflammation and leukocyte recruitment.^[6] Similarly, variants such as rs2794520 and rs2808629 are in perfect linkage disequilibrium and contribute to variability in C-reactive protein (CRP) levels, a widely used marker of systemic inflammation.^[6] High CRP levels, also influenced by rs1205 and rs12093699 , signify ongoing inflammation that can alter cellular environments, potentially affecting the signaling and function of adhesion receptors like ADGRG5 by modulating cell adhesion and migration processes. ^[6] Another inflammatory marker, macrophage inflammatory protein-1 beta (MIP-1beta), is influenced by rs4796217 , with each minor allele decreasing its levels, thereby impacting immune cell trafficking and inflammatory responses. ^[11]

Beyond direct inflammatory markers, variants affecting hemostatic factors and metabolic pathways also contribute to systemic health and can indirectly influence cellular adhesion and receptor function. For example, rs561241 , residing near the F7gene, is strongly associated with circulating levels of Factor VII, a crucial component of the coagulation cascade.^[1] Maintaining proper hemostasis is vital for vascular integrity, and disruptions can lead to inflammatory responses and altered endothelial cell behavior, which are contexts where ADGRG5 might play a role in regulating cell-matrix interactions. Furthermore, variants in genes like GLUT9, including rs16890979 , are significantly associated with serum uric acid levels.^[12]Dysregulation of uric acid metabolism can contribute to inflammatory conditions and metabolic disorders, which in turn can impact cellular signaling pathways, including those involving adhesion G protein-coupled receptors that mediate cellular responses to changes in the microenvironment.

Other genetic variations impact critical metabolic and transport processes. SNPs in genes such as APOA5 (rs6589566 ) are linked to lipid metabolism and dyslipidemia, a risk factor for cardiovascular disease.^[13]Similarly, the glucokinase regulator gene (GCKR), with variants like rs780094 , influences glucose and lipid metabolism, which are fundamental to cellular energy balance and function.^[13] The SRPRB gene, located near TF, contains variants like rs3811647 and rs10512913 that are associated with serum transferrin levels, a protein essential for iron transport.^[14] These metabolic and transport pathways are interconnected, and their dysregulation can create cellular stress or alter cellular communication, potentially affecting the expression or activity of adhesion receptors like ADGRG5 by modulating the cellular environment and its capacity for adhesion and signaling.

Key Variants

RS ID	Gene	Related Traits
rs201263987	CFH	platelet endothelial cell adhesion molecule measurement interleukin-34 measurement receptor-type tyrosine-protein kinase flt3 measurement adhesion G-protein coupled receptor G5 measurement ribonuclease H1 measurement

Biological Background

Platelet Adhesion and Activation in Hemostasis

Platelets are critical cellular components in the process of hemostasis, which is the body’s mechanism to stop bleeding. This intricate process involves a series of steps including platelet adhesion, activation, and aggregation to form a stable clot at the site of vascular injury. Platelet activation and subsequent aggregation can be induced by various physiological agonists, such as adenosine diphosphate (ADP), collagen, and epinephrine.^[1]These triggers initiate distinct signaling pathways within platelets, leading to changes in their shape, secretion of pro-thrombotic factors, and ultimately their clumping together. The importance of these cellular functions is highlighted by their study as key hematological phenotypes.^[1]

The cellular and tissue context of platelet function extends beyond the bloodstream. Platelet biology is closely intertwined with other cell types, including vascular smooth muscle cells and renal mesangial cells, where certain molecules involved in platelet processes are also expressed.^[1] This broad expression suggests a wider role for these molecular players in maintaining vascular integrity and other physiological functions beyond immediate clot formation. Understanding the mechanisms governing platelet adhesion and activation is fundamental to comprehending both normal hemostasis and the development of related pathophysiological conditions.

Genetic Influences on Platelet Reactivity

Genetic variations, particularly single nucleotide polymorphisms (SNPs), play a significant role in modulating individual differences in hemostatic factors and hematological phenotypes. Genome-wide association studies (GWAS) have identified specific genetic loci associated with varying levels of platelet aggregation in response to different stimuli. For instance, the SNPrs10500631 has been found to be associated with ADP-induced, collagen-induced, and epinephrine-induced platelet aggregation levels. ^[1] Similarly, rs10514919 demonstrates association with ADP-induced, collagen-induced, and epinephrine-induced platelet aggregation. ^[1]

These genetic associations suggest that inherited factors contribute to the variability in how an individual’s platelets respond to activating signals, thereby influencing their overall hemostatic profile. While the specific gene functions and regulatory elements underlying these associations require further investigation, the identification of such SNPs provides valuable insights into the genetic architecture of platelet reactivity. The presence of these genetic markers near or within candidate genes involved in hemostasis underscores the complex regulatory networks that govern these essential biological processes. ^[1]

Molecular Signaling in Platelet Biology

At the molecular level, the activation of platelets by agonists like ADP, collagen, and epinephrine is mediated through specific cell surface receptors that initiate intracellular signaling cascades. As an adhesion G protein-coupled receptor, ADGRG5 belongs to a large family of receptors that are central to cellular communication, translating extracellular signals into intracellular responses. These receptors typically interact with G proteins, which then activate or inhibit downstream effectors, leading to a diverse array of cellular outcomes, including changes in cell shape, motility, and adhesion. The activation of these signaling pathways is critical for coordinating the rapid and precise responses required during hemostasis.

The involvement of G protein-coupled receptors in mediating cellular adhesion and signaling pathways is a fundamental aspect of many biological processes. In the context of platelet function, these molecular mechanisms are responsible for transducing signals from circulating agonists or components of the extracellular matrix, such as collagen, into the biochemical events that drive platelet aggregation. Dysregulation within these intricate signaling networks, whether due to genetic variation or other factors, can alter platelet reactivity and consequently impact overall hemostatic balance.

Systemic Impact on Hematological Phenotypes

The coordinated function of platelets and other hemostatic factors is essential for maintaining physiological homeostasis throughout the body. Disruptions in platelet aggregation or the balance of coagulation factors can lead to various pathophysiological processes, ranging from excessive bleeding to thrombotic disorders. The study of hemostatic factors and hematological phenotypes, such as platelet aggregation levels, provides a systemic view of how these molecular and cellular processes contribute to overall health.^[1]

Beyond platelets, other critical biomolecules like Factor VII (F7) and fibrinogen are also key components of the coagulation cascade, and their levels are subject to genetic influence. ^[1] For instance, specific SNPs near the F7gene have been strongly associated with circulating levels of Factor VII.^[1] Similarly, genetic variations can impact fibrinogen levels. ^[1] The interplay between these various hemostatic components, their genetic determinants, and their tissue-specific expressions ultimately dictates the systemic consequences for an individual’s susceptibility to bleeding or clotting disorders.

Clinical Relevance

References

[1] Yang, Q et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. S12.

[2] Vasan, R. S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. S1, 2007, S2. PMID: 17903301.

[3] Pare, G., et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genetics, vol. 4, no. 7, 2008, e1000118. PMID: 18604267.

[4] O’Donnell, C. J., et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. S1, 2007, S7. PMID: 17903303.

[5] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” The American Journal of Human Genetics, vol. 83, no. 5, 2008, pp. 520–531. PMID: 18940312.

[6] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. S1, 2007, S11. PMID: 17903293.

[7] Devlin, B., and Roeder, K. “Genomic control for association studies.” Biometrics, vol. 55, no. 4, 1999, pp. 997–1004.

[8] Panico, S., et al. “Variability of serum soluble intercellular adhesion molecule-1 measurements attributable to a common polymorphism.” Clinical Chemistry, vol. 50, no. 12, 2004, pp. 2185–2187.

[9] Ricklin, Daniel, et al. “Complement: a key system in innate immunity and inflammation.” Trends in Immunology, vol. 32, no. 11, 2011, pp. 505-512.

[10] Langenhan, Tobias, et al. “Adhesion G protein-coupled receptors: a new class of drug targets.” Nature Reviews Drug Discovery, vol. 18, no. 12, 2019, pp. 891-908.

[11] Melzer, David, et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, pp. e1000072.

[12] McArdle, Patrick F., et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.”Arthritis & Rheumatism, vol. 60, no. 9, 2009, pp. 2812-2818.

[13] Wallace, Cathryn, et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”The American Journal of Human Genetics, vol. 82, no. 1, 2008, pp. 139-149.

[14] Benyamin, Beben, et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”The American Journal of Human Genetics, vol. 83, no. 6, 2008, pp. 683-688.