Carboxypeptidase E
Background
Section titled “Background”Carboxypeptidase E (CPE), also known as enkephalin convertase, is a metalloprotease enzyme crucial for the post-translational processing of various prohormones and proneuropeptides. It plays a significant role in the maturation of these precursor molecules into their biologically active forms.
Biological Basis
Section titled “Biological Basis”CPEfunctions as a “trimming” enzyme, responsible for removing basic amino acid residues (typically lysine or arginine) from the C-terminus of peptide intermediates. This action occurs after initial cleavage by endoproteases, such as prohormone convertases. This precise enzymatic activity is essential for the final maturation of a wide array of peptides, including many hormones, neuropeptides, and growth factors. It is primarily found within the secretory granules of neuroendocrine cells, where it completes the final steps of peptide biosynthesis.
Clinical Relevance
Section titled “Clinical Relevance”Variations or dysregulation in CPE activity can have notable clinical implications. For example, mutations in the CPEgene have been associated with various conditions, including forms of obesity, impaired glucose homeostasis, and certain neurodevelopmental disorders. This is due to its critical role in processing key peptides such as insulin, glucagon, and neuropeptides involved in appetite regulation and neurological functions. UnderstandingCPE is therefore important for research into metabolic and neurological diseases.
Social Importance
Section titled “Social Importance”The study of CPEcontributes broadly to our understanding of human endocrinology and neuroscience. Insights into its function in the production of hormones and neuropeptides can inform the development of new therapeutic strategies for common conditions like type 2 diabetes, obesity, and specific neurological disorders, thereby potentially improving public health outcomes and quality of life.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The moderate sample sizes within individual cohorts limit the statistical power to detect genetic associations with modest effect sizes, potentially leading to false negative findings and an underestimation of the full genetic contribution to a trait. [1] Conversely, the extensive multiple statistical testing inherent in genome-wide association studies increases the likelihood that some reported associations may represent false positive findings, especially those lacking independent replication. [1] Furthermore, the reliance on a subset of available SNPs, coupled with limitations in imputation quality based on specific HapMap builds, can result in incomplete genomic coverage, meaning some causal variants or genes may be missed and comprehensive candidate gene analysis is hindered. [2] This partial coverage also impacts the ability to replicate previously reported findings, as different studies might identify distinct SNPs in linkage disequilibrium with an unknown causal variant, rather than the same specific SNP. [3]
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”The generalizability of findings is primarily limited by the demographic characteristics of the study cohorts, which are predominantly of European ancestry, making it uncertain how these genetic associations would apply to or manifest in other ethnic groups. [4]Phenotypic characterization also presents challenges; for example, using cystatin C as a marker of kidney function may not fully isolate kidney function from cardiovascular disease risk, and TSH alone may not accurately reflect overall thyroid function without free thyroxine measures.[4] While efforts were made to standardize measurements through techniques like averaging repeated observations or applying transformations such as natural log or Winsorized residuals, these methods can influence the observed genetic effects and their comparability across diverse study designs. [1]
Unexplored Genetic and Environmental Factors
Section titled “Unexplored Genetic and Environmental Factors”Current investigations often did not undertake comprehensive analyses of gene-environment interactions, despite evidence that genetic variants, such as those in ACE and AGTR2, can influence phenotypes in a context-specific manner modulated by environmental factors like dietary salt intake. [5] Moreover, the practice of performing only sex-pooled analyses, rather than sex-specific assessments, risks overlooking genetic associations that may be unique to either males or females, leading to undetected yet potentially significant findings. [2] The stringent statistical corrections required for genome-wide multiple phenotypes can also limit the detection of additional trans effects, such as those nominally associated with FGB (rs6056 ) or CCL2 (rs1024611 ), thereby leaving further knowledge gaps regarding the complete genetic architecture underlying complex traits. [6]
Variants
Section titled “Variants”Genetic variations across several genes contribute to a spectrum of physiological processes, including inflammation, lipid metabolism, and immune responses, which can indirectly or directly influence the activity and implications of carboxypeptidase E (CPE). Carboxypeptidase E is an enzyme critical for processing peptide hormones and neuropeptides, playing a vital role in neuroendocrine function, glucose homeostasis, and appetite regulation. Variations in genes likeHNF1A and APOEhave been linked to C-reactive protein levels and lipid profiles, respectively, highlighting their broad metabolic impact.
One significant variant, rs2393775 in the HNF1Agene, is strongly associated with C-reactive protein (CRP) levels, a key marker of inflammation.[7] The HNF1Agene encodes Hepatocyte Nuclear Factor 1 Alpha, a transcription factor essential for the development and function of the liver, pancreas, and other tissues, influencing gene expression related to glucose and lipid metabolism.rs2393775 is found within the first intron of HNF1A, and its association with CRP suggests a role in systemic inflammation, a factor that can modulate the expression or activity of various enzymes, including those in the carboxypeptidase family. [7] Similarly, the APOE gene, particularly the rs429358 variant (part of the epsilon 4 allele), is well-known for its strong influence on lipid metabolism and cardiovascular disease risk.APOEis a component of lipoprotein particles, facilitating their uptake by cells, andrs429358 can alter this function, leading to elevated cholesterol levels. [8] The APOEgene region has also been associated with C-reactive protein levels, indicating its broader involvement in inflammatory pathways that could interact withCPE-regulated processes. [7] Beyond these, the CFH gene, encoding Complement Factor H, plays a crucial role in regulating the complement system, a part of the innate immune response, and variations like rs7539005 can impact inflammation and immune-mediated conditions, potentially affecting systemic metabolic balance that CPE contributes to.
Other variants contribute to diverse biological functions that collectively impact metabolic and immune health. The MSMO1 gene, involved in cholesterol biosynthesis, has variants such as rs1550270 and rs142496142 that could influence lipid profiles and cellular membrane composition. These changes might indirectly affect the efficiency of peptide processing or signaling pathways whereCPE plays a role. Similarly, the ATXN2 gene, with variants like rs597808 , is linked to neurodegenerative disorders but also shows associations with metabolic traits, including insulin resistance and type 2 diabetes, conditions closely tied toCPE’s function in glucose homeostasis. TheMRC1 gene, encoding the Mannose Receptor C-Type 1, is crucial for innate immunity, pathogen recognition, and antigen presentation, and its variant rs56278466 could modulate inflammatory responses that impact metabolic health and enzyme activities like CPE.
Variations within the CPE gene itself, such as rs142920534 , rs72529533 , and rs545284100 , are directly relevant to the enzyme’s function. While specific associations for these variants are not detailed here, such genetic changes can affect CPEexpression, protein stability, or enzymatic activity, thereby altering the processing of prohormones and neuropeptides. This can have downstream effects on critical functions like insulin secretion, satiety signaling, and stress responses. TheCTRB2 gene, encoding Chymotrypsinogen B2, is involved in digestive processes, and its variant rs72802352 might influence gut hormone processing or nutrient absorption, which can indirectly interact with the broader metabolic landscape regulated byCPE. Furthermore, variants in the ABO gene, which determines blood group antigens, including rs635634 , are known to influence plasma levels of various proteins and inflammatory markers. The ABO gene encodes glycosyltransferase enzymes that modify cell surface antigens. [9] Variations in ABOhave been linked to levels of TNF-alpha, an inflammatory cytokine, suggesting a role in immune and inflammatory responses that could influence overall metabolic health and the physiological context in whichCPE operates. [6]
There is no information about carboxypeptidase E in the provided research materials.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1550270 rs142496142 | MSMO1 | heel bone mineral density blood protein amount carboxypeptidase e measurement body height |
| rs142920534 | CPE | carboxypeptidase e measurement |
| rs7539005 | CFH | t-SNARE domain-containing protein 1 measurement glycosyltransferase 8 domain-containing protein 1 measurement kallikrein-6 measurement OCIA domain-containing protein 1 measurement protein measurement |
| rs72529533 rs545284100 | CPE | carboxypeptidase e measurement |
| rs2393775 | HNF1A | transferrin glycosylation measurement testosterone measurement sex hormone-binding globulin measurement level of meprin A subunit beta in blood Cholecystitis |
| rs597808 | ATXN2 | gastroesophageal reflux disease systolic blood pressure, alcohol drinking diastolic blood pressure, alcohol drinking colorectal cancer, colorectal adenoma systemic lupus erythematosus |
| rs429358 | APOE | cerebral amyloid deposition measurement Lewy body dementia, Lewy body dementia measurement high density lipoprotein cholesterol measurement platelet count neuroimaging measurement |
| rs56278466 | MRC1 | aspartate aminotransferase measurement liver fibrosis measurement ADGRE5/VCAM1 protein level ratio in blood CD200/CLEC4G protein level ratio in blood HYOU1/TGFBR3 protein level ratio in blood |
| rs72802352 | CTRB2 | type 2 diabetes mellitus carboxypeptidase e measurement level of extracellular superoxide dismutase [Cu-Zn] in blood |
| rs635634 | ABO - Y_RNA | leukocyte quantity neutrophil count, eosinophil count granulocyte count Ischemic stroke neutrophil count, basophil count |
References
Section titled “References”[1] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S11.
[2] 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, suppl. 1, 2007, p. S10.
[3] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 40, no. 12, 2008, pp. 1394-402.
[4] Hwang, S. J., et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S12.
[5] 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 Med Genet, vol. 8, suppl. 1, 2007, p. S2.
[6] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[7] Reiner, AP. et al. “Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein.”Am J Hum Genet, 2008.
[8] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008.
[9] 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 Genet, 2008.