Adamts13
Introduction
Section titled “Introduction”ADAMTS13 (A Disintegrin And Metalloproteinase with ThromboSpondin Type 1 Motif, 13) is an enzyme primarily recognized for its critical role in regulating blood clot formation. It belongs to the ADAMTS family of metalloproteases, which are involved in various biological processes, including the organization and breakdown of components within the extracellular matrix.
Biological Basis
Section titled “Biological Basis”The primary biological function of ADAMTS13 is to cleave large multimers of von Willebrand factor (VWF). VWF is a crucial protein in hemostasis, the process that stops bleeding. It mediates the adhesion and aggregation of platelets at sites of vascular injury. When VWF is released from endothelial cells, it can form long, highly active chains that can spontaneously bind platelets, potentially leading to excessive clot formation. ADAMTS13 precisely cuts these large VWF multimers into smaller, less active forms, thereby preventing uncontrolled platelet aggregation and the formation of microscopic blood clots within small blood vessels.
Clinical Relevance
Section titled “Clinical Relevance”Deficiencies or dysfunction of ADAMTS13are clinically significant. A severe deficiency can lead to Thrombotic Thrombocytopenic Purpura (TTP), a rare but life-threatening disorder. TTP is characterized by the widespread formation of microvascular thrombi, which can cause a low platelet count (thrombocytopenia), a type of anemia called microangiopathic hemolytic anemia, and damage to organs, particularly the brain and kidneys. TTP can be inherited due to genetic mutations in theADAMTS13 gene (known as hereditary TTP or Upshaw-Schulman syndrome) or acquired through the development of autoantibodies that inhibit ADAMTS13 activity (immune-mediated TTP). Less severe reductions in ADAMTS13activity have also been investigated for their potential involvement in other thrombotic disorders and cardiovascular diseases.
Social Importance
Section titled “Social Importance”The understanding of ADAMTS13 and its role in TTP has profoundly impacted the diagnosis and treatment of this severe condition. Early and accurate diagnosis of TTP, often involving the measurement of ADAMTS13 activity, is essential for timely intervention with life-saving therapies such as plasma exchange. Ongoing research into ADAMTS13 continues to improve diagnostic tools, develop new therapeutic strategies, and explore its broader implications in other vascular diseases, ultimately contributing to better patient outcomes and public health.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic association studies, particularly genome-wide association studies (GWAS), are subject to several methodological and statistical constraints that can influence the interpretation of findings. Many studies have limited statistical power to detect modest genetic effects, especially when considering the extensive multiple testing inherent in a genome-wide scan. [1]For instance, sample sizes for specific phenotypes can be relatively modest, such as 673–984 participants for subclinical atherosclerosis measures[2] and moderate cohort sizes can increase susceptibility to false negative findings. [3] Identifying additional genetic variants and improving the power for gene discovery often necessitates significantly larger samples. [4]
Replication of initial findings is a critical step in validating genetic associations, yet many reported associations are not consistently replicated across studies. This lack of replication can be attributed to several factors, including false positive findings in earlier reports, key differences between study cohorts, or insufficient statistical power in the replication studies, leading to false negative results. [3]Furthermore, different studies may identify distinct associated single nucleotide polymorphisms (SNPs) within the same gene region; these SNPs might each be in strong linkage disequilibrium with an unknown causal variant but not with each other, or they could reflect the presence of multiple causal variants within the same gene.[5] The use of older genotyping arrays that cover only a subset of all available SNPs in reference panels like HapMap can lead to missing potentially important genes due to incomplete coverage [6] and such data may not be comprehensive enough for detailed candidate gene investigations. [6] While imputation methods are employed to infer missing genotypes, these processes introduce estimated error rates, typically ranging from 1.46% to 2.14% per allele [7] which can affect the accuracy of associations. Additionally, strict filtering criteria, such as excluding SNPs with minor allele frequencies below 1% or those deviating significantly from Hardy-Weinberg equilibrium [8] may inadvertently remove rare but functionally significant variants.
Generalizability and Phenotype Assessment Challenges
Section titled “Generalizability and Phenotype Assessment Challenges”The generalizability of findings from genetic studies is often limited by the demographic characteristics of the study populations. Many cohorts are predominantly composed of individuals of white European descent and are often middle-aged to elderly. [3] Consequently, the observed genetic associations may not be broadly applicable to younger individuals or populations of different ethnic or racial backgrounds. [3] While researchers employ strategies like principal component analysis and genomic control to account for population stratification [8] residual stratification within seemingly homogenous populations can still confound results. Furthermore, the selection of participants, such as the collection of DNA during later examination cycles, may introduce a survival bias, impacting the representativeness of the cohort. [3]
Challenges in phenotype assessment can also impact the accuracy and interpretation of genetic associations. For instance, averaging physiological traits across multiple examinations, especially when these examinations span two decades and involve different measurement equipment, can introduce misclassification. [1] This averaging strategy also implicitly assumes that the same genetic and environmental factors influence traits uniformly across a wide age range, which may not be accurate, potentially masking age-dependent gene effects. [1] Moreover, the timing and conditions of biological sample collection can introduce significant confounders; for example, variations in serum markers are known to be influenced by the time of day blood is collected and by an individual’s menopausal status. [9] Inconsistent collection protocols across different study phases or cohorts can therefore obscure true genetic effects. [9]
Environmental Interactions and Remaining Knowledge Gaps
Section titled “Environmental Interactions and Remaining Knowledge Gaps”A significant limitation in understanding the complete genetic architecture of complex traits is the infrequent investigation of gene-environment interactions. Genetic variants often influence phenotypes in a context-specific manner, with their effects being modulated by various environmental factors. [1] For example, associations between genes like ACE and AGTR2and left ventricular mass have been shown to vary with dietary salt intake.[1] However, many current studies do not undertake a comprehensive investigation of such interactions [1]which can lead to an incomplete understanding of the true genetic contributions and the overall “missing heritability” for complex traits. Without accounting for these environmental modulators, the full picture of how genes influence health and disease remains obscured.
Beyond identifying associations, a fundamental challenge in GWAS is the prioritization of significant SNPs for further investigation and functional validation. [3] Many moderately strong associations observed in initial scans might represent false-positive findings, underscoring the need for rigorous follow-up. The ultimate validation of genetic associations requires not only replication in independent cohorts but also extensive functional studies to elucidate the underlying biological mechanisms. [3] This process highlights a substantial remaining knowledge gap between statistical association and a comprehensive biological understanding of how specific genetic variants contribute to phenotypic variation.
Variants
Section titled “Variants”The ADAMTS13 gene encodes A disintegrin and metalloproteinase with thrombospondin motifs 13, a crucial enzyme responsible for cleaving von Willebrand factor (vWF) multimers in the bloodstream. This proteolytic activity is essential for regulating blood clot formation, preventing the accumulation of ultra-large vWF multimers that could lead to spontaneous and uncontrolled thrombosis. Variants such as rs41314453 and rs3118667 within or near the ADAMTS13 gene can influence the enzyme’s expression levels or its catalytic efficiency, thereby affecting its ability to process vWF. [6] Such genetic variations are important because altered ADAMTS13 activity is directly linked to conditions like thrombotic thrombocytopenic purpura (TTP) and can modulate an individual’s overall risk for thrombotic events. [10]
The variant rs139911703 is associated with the OBP2B gene, which is categorized as an Odorant Binding Protein 2B pseudogene. While pseudogenes generally do not produce functional proteins, variants within these regions can sometimes impact the expression of neighboring functional genes or modulate regulatory networks through mechanisms like competing for microRNAs or acting as genomic decoys. [4] Although the direct mechanism linking OBP2B to ADAMTS13or related coagulation traits is not extensively characterized, genetic variations in non-coding regions can broadly influence complex biological pathways. These indirect effects highlight how genetic architecture, even in pseudogenes, can contribute to an individual’s predisposition to various phenotypes, including those related to cardiovascular health.[2]
Another variant, rs10456544 , is located in the SUPT3H gene, which encodes the SPT3 homolog, a component of the SAGA (Spt-Ada-Gcn5-acetyltransferase) complex. This complex is a vital transcriptional co-activator involved in chromatin remodeling and the initiation of gene transcription across the genome. [11] Variants in genes like SUPT3H can potentially alter the efficiency of gene expression, thereby influencing the production of numerous proteins, including those involved in coagulation and inflammation pathways that interact with ADAMTS13 activity. Therefore, SUPT3H variants could indirectly contribute to the variability observed in hemostatic factor levels or the risk of thrombotic disorders by broadly affecting the cellular machinery for gene regulation. [12]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs34054981 rs71503194 rs74715985 | ADAMTS13 | adamts13 measurement |
| rs28615428 | SLC2A6 | adamts13 measurement |
Causes
Section titled “Causes”Genetic Predisposition and Polygenic Influences
Section titled “Genetic Predisposition and Polygenic Influences”Genetic factors play a substantial role in determining an individual’s susceptibility to various traits and conditions, often through inherited variants and polygenic risk. For instance, specific single nucleotide polymorphisms (SNPs) have been identified that are associated with subclinical atherosclerosis, such asrs1376877 located in the ABI2 gene or rs3849150 near LRRC18. [2] Many complex traits, like dyslipidemia, are influenced by common variants across numerous genetic loci, including regions such as APOE-APOC1-APOC4-APOC2, NCAN, CILP2, PBX4, and FADS1-FADS2-FADS3, highlighting a polygenic architecture where multiple genes collectively contribute to risk. [4] Beyond structural genes, variations in genes like CPN1, SAMM50 (rs3761472 ), and PNPLA3 have been linked to plasma levels of liver enzymes, while SLC2A9 (rs16890979 , rs6449213 ), ABCG2 (rs2231142 ), and SLC17A3 (rs1165205 ) are associated with uric acid concentrations.[8]
Environmental Triggers and Early Life Modulators
Section titled “Environmental Triggers and Early Life Modulators”Environmental factors, including aspects of diet and early life experiences, can significantly modulate an individual’s biological profile. For example, theFADS1gene cluster, which is involved in long-chain polyunsaturated fatty acid metabolism, contains polymorphisms likers174548 that influence an individual’s ability to metabolize certain fatty acids. [12]This genetic variation has been shown to moderate the association between breastfeeding and intelligence quotient (IQ), suggesting that the unique fatty acids available in breast milk represent an early life environmental factor whose impact is shaped by genetic predisposition.[12] Such interactions can influence critical developmental processes, potentially affecting cellular membrane fluidity and the function of membrane-bound receptors. [12]
Gene-Environment Interplay
Section titled “Gene-Environment Interplay”The interaction between an individual’s genetic makeup and their environment is a crucial determinant of phenotypic expression. A notable example is how genetic variation in the FADS1gene cluster can moderate the association between breastfeeding and intelligence quotient, illustrating that the effect of an environmental exposure (breastfeeding) is dependent on the individual’s genetic ability to process specific nutrients.[12] Furthermore, studies have investigated how a genetic risk score, derived from multiple SNPs associated with a trait—such as rs16890979 in SLC2A9, rs2231142 in ABCG2, and rs1165205 in SLC17A3for uric acid levels—interacts with various environmental factors.[8] These gene-by-environment interactions highlight the complex interplay where genetic susceptibility is either exacerbated or mitigated by external influences.
Comorbidities and Broader Biological Context
Section titled “Comorbidities and Broader Biological Context”Various comorbidities and an individual’s broader health context can significantly contribute to the manifestation or progression of specific traits. Genetic variants or metabotypes associated with one condition often show correlations with other complex diseases. For instance, certain genetic profiles have been linked not only to metabolic traits but also to the risk of conditions such as bipolar disorder, coronary artery disease, Crohn’s disease, hypertension, rheumatoid arthritis, type 1 diabetes mellitus, and type 2 diabetes mellitus.[12] These associations suggest that shared genetic pathways, metabolic dysregulation, or systemic inflammatory processes underlying these comorbidities can collectively influence an individual’s health status and predisposition to various biological traits.
References
Section titled “References”[1] Vasan, Ramachandran 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. Suppl 1, 2007, p. S2.
[2] O’Donnell, Christopher 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. Suppl 1, 2007, p. S12.
[3] Benjamin, Emelia J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S10.
[4] Kathiresan, S et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008. PMID: 19060906.
[5] Sabatti, Chiara et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 40, no. 12, 2008, pp. 1394-1402.
[6] Yang, Qiong, et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet 8.Suppl 1 (2007): S9.
[7] Willer, Cristen J. et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nature Genetics, vol. 40, no. 2, 2008, pp. 161-169.
[8] Dehghan, A et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, 2008. PMID: 18834626.
[9] Benyamin, B et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, 2008. PMID: 19084217.
[10] Hwang, Shih-Jen, et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet 8.Suppl 1 (2007): S10.
[11] Yuan, X et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, 2008. PMID: 18940312.
[12] Gieger, C et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, 2008. PMID: 19043545.