Cysteinylglycine Disulfide
Introduction
Section titled “Introduction”Cysteinylglycine disulfide is a dipeptide derivative formed from two molecules of cysteinylglycine linked by a disulfide bond. Cysteinylglycine itself is an intermediate product in the gamma-glutamyl cycle, a critical metabolic pathway primarily involved in the synthesis and degradation of glutathione, the body’s major endogenous antioxidant. The formation of a disulfide bond typically indicates an oxidative process, suggesting that cysteinylglycine disulfide may arise under conditions of oxidative stress or through specific enzymatic reactions.
Biological Basis
Section titled “Biological Basis”In the gamma-glutamyl cycle, glutathione (gamma-L-glutamyl-L-cysteinylglycine) is broken down by the enzyme gamma-glutamyl transpeptidase (GGT) into glutamate and cysteinylglycine. Subsequently, cysteinylglycine is cleaved by dipeptidases into its constituent amino acids, cysteine and glycine, which can then be recycled for glutathione synthesis. The presence of cysteinylglycine disulfide implies the oxidation of the thiol groups of two cysteinylglycine molecules. This oxidative modification can occur spontaneously in the presence of reactive oxygen species or be enzymatically regulated, highlighting its potential role as a marker for redox balance within biological systems. Studies have utilized advanced techniques such as mass spectrometry to identify and quantify various metabolites, including dipeptides and their derivatives, within human serum, providing insights into metabolic pathways . Furthermore, without independent replication in diverse cohorts, many statistically significant p-values may represent false positive findings, underscoring the exploratory nature of initial associations.[1] A significant concern is the common practice of presenting p-values unadjusted for the extensive number of comparisons performed across the genome, which can inflate the perceived statistical significance of associations. [2] Initial effect sizes reported from discovery stages may also be overestimated, requiring validation in larger, independent samples for more accurate estimation. [2]
The coverage of single nucleotide polymorphisms (SNPs) on genotyping arrays can also be a limiting factor. Studies using less dense arrays, such as 100K SNP platforms, may not sufficiently cover all gene regions, potentially failing to identify true associations that could be captured by newer, more comprehensive arrays.[3] This incomplete coverage, combined with the complexities of imputation methods used to infer missing genotypes, introduces a degree of estimation error, which, while generally low, can still impact the accuracy of genotype-phenotype associations. [4] Consequently, while GWAS are powerful tools for discovery, these statistical and methodological considerations necessitate cautious interpretation and rigorous follow-up.
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”A notable limitation of many genetic association studies is the restricted generalizability of their findings due to cohort characteristics. Many cohorts are predominantly composed of individuals of white European descent and often span a specific age range, such as middle-aged to elderly populations. [1] This lack of ethnic and age diversity makes it uncertain how the identified genetic associations would apply to younger individuals or other ethnic and racial groups, highlighting a significant gap in understanding broader population effects. [1] Additionally, the timing of DNA collection in certain studies, particularly in older cohorts, may introduce a survival bias, further limiting the applicability of findings to the general population. [1]
The precise characterization and measurement of phenotypes also present challenges. For instance, while certain markers like cystatin C are used for kidney function, they may also reflect other health risks, such as cardiovascular disease, beyond their primary intended measure.[5]Similarly, relying on indicators like TSH without comprehensive thyroid hormone panels or reliable assessments of thyroid disease can provide an incomplete picture of thyroid function.[5] Many protein levels and other biomarkers are not normally distributed, necessitating complex statistical transformations to achieve appropriate analytical conditions, which can influence the interpretation of genetic associations. [6] Furthermore, the exclusion of individuals on specific medications, such as lipid-lowering therapies, from analyses can influence the observed genetic effects on related traits. [7]
Environmental Confounding and Remaining Knowledge Gaps
Section titled “Environmental Confounding and Remaining Knowledge Gaps”Environmental factors and gene-environment interactions can significantly influence biomarker levels and confound genetic associations. For example, variations in serum markers like iron status are known to be affected by the time of day blood samples are collected and an individual’s menopausal status. [2] While some studies attempt to control for these variables, their pervasive influence means that unidentified or unmeasured environmental confounders could still impact the observed genetic associations. This complexity underscores the challenge of isolating purely genetic effects, as environmental exposures can modify the expression or impact of genetic variants.
Despite the comprehensive nature of genome-wide scans, specific study designs may unintentionally overlook important associations. A focus on multivariable models, while statistically robust, might lead to missing significant bivariate associations between individual SNPs and traits. [5] The ultimate validation of genetic findings necessitates not only replication in independent cohorts but also functional studies to elucidate the biological mechanisms by which genetic variants exert their effects. [1] Consequently, while GWAS have been instrumental in identifying numerous genetic loci, a substantial portion of the heritability for many complex traits often remains unexplained, indicating the presence of additional, yet-to-be-discovered genetic or environmental factors, or complex interactions among them.
Variants
Section titled “Variants”Genetic variations within genes involved in amino acid metabolism, transporter proteins, and cellular stress response pathways can significantly influence the levels and redox state of cysteinylglycine, including its disulfide form. The dipeptide cysteinylglycine is a crucial intermediate in the gamma-glutamyl cycle and a precursor to cysteine, which is essential for glutathione synthesis, the body’s primary antioxidant. Therefore, polymorphisms affecting these pathways can have broad implications for cellular redox balance and detoxification.
The gene DPEP1(Dipeptidase 1) plays a critical role in the metabolism of dipeptides, including cysteinylglycine, by hydrolyzing them into their constituent amino acids, cysteine and glycine. Cysteine is a rate-limiting precursor for the synthesis of glutathione, making the activity ofDPEP1 directly influential on cellular redox state. Variants such as rs1126464 and rs409170 in DPEP1could alter the enzyme’s efficiency, thereby affecting the availability of cysteine for glutathione production and the overall balance of cysteinylglycine, including its disulfide form. Similarly,ABCC1(ATP Binding Cassette Subfamily C Member 1), also known as MRP1, functions as an efflux pump that expels a wide range of substrates, notably glutathione conjugates and oxidized glutathione (GSSG), from cells. Genetic variations likers924135 , rs246223 , and rs60782127 within ABCC1 may impact its transport capabilities, leading to altered intracellular levels of GSSG and a shift in the cellular redox environment. [8]Such changes in glutathione metabolism directly influence the concentration and redox state of cysteinylglycine and its disulfide, which are integral to antioxidant defense and cellular detoxification pathways.
Further impacting cellular resilience, the FANCA(Fanconi Anemia Complementation Group A) gene is a crucial component of the Fanconi anemia pathway, essential for repairing damaged DNA and maintaining genomic stability in the face of cellular stress. Given that oxidative stress can induce DNA damage, variations likers7204478 , associated with both ZNF276 (Zinc Finger Protein 276) and FANCA, could affect the cell’s ability to cope with such stress, thereby influencing the demand for and metabolism of antioxidants like glutathione and its precursor, cysteinylglycine.[1] Concurrently, SLC23A3(Solute Carrier Family 23 Member 3) is a transporter gene, potentially involved in the cellular uptake of vitamin C, a potent antioxidant. The variantrs192756070 in SLC23A3might modulate the availability of this vital antioxidant, indirectly impacting the cellular redox balance and the interconversion of cysteinylglycine and its disulfide form. The intergenic variantrs2139455 , located between DPEP1 and CHMP1A (Charged Multivesicular Body Protein 1A), may represent a regulatory element or a locus where the combined influence of nearby genes impacts metabolic pathways crucial for maintaining cellular homeostasis. [7]
Beyond direct metabolic roles, several genes influence cellular health and stress responses through broader mechanisms. CHMP1A (Charged Multivesicular Body Protein 1A), with variants rs164749 and rs58290281 , is involved in endosomal trafficking and membrane remodeling as part of the ESCRT-III complex. While not directly metabolizing cysteinylglycine, its role in maintaining cellular integrity and signaling could indirectly affect how cells manage oxidative stress.[6] Similarly, CPNE7 (Copine 7), with variant rs75032725 , and AGBL1(ATP/GTP Binding Protein-Like 1), with variantrs6496346 , participate in diverse cellular signaling and protein modification pathways. These broader cellular functions can influence overall metabolic homeostasis and inflammatory responses, which in turn may impact the redox environment and the levels of cysteinylglycine disulfide.[9] The C1QTNF9 (C1q and TNF Related 9) gene, featuring variant rs9511186 , is associated with metabolic regulation and inflammation, which are known to influence systemic oxidative stress and the balance of redox-active molecules. Lastly, the intergenic variant rs117315039 between COPS8 (Constitutive Photomorphogenic 8), a component of the COP9 signalosome regulating protein degradation, and COL6A3(Collagen Type VI Alpha 3 Chain), an extracellular matrix protein, may also subtly affect cellular responses to stress or tissue maintenance, thereby indirectly linking to the complex interplay of metabolic health and cysteinylglycine disulfide.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1126464 rs409170 | DPEP1 | diastolic blood pressure hypertension systolic blood pressure body height osteoarthritis |
| rs164749 rs58290281 | CHMP1A | CD69/CHMP1A protein level ratio in blood CDKN2D/CHMP1A protein level ratio in blood CHMP1A/CRADD protein level ratio in blood CHMP1A/DCTN1 protein level ratio in blood CHMP1A/EIF4B protein level ratio in blood |
| rs2139455 | DPEP1 - CHMP1A | cysteinylglycine disulfide measurement cys-gly, oxidized measurement |
| rs924135 rs246223 rs60782127 | ABCC1 | basophil count interleukin-2 receptor subunit alpha measurement nonanoylcarnitine (C9) measurement coagulation factor X amount serum metabolite level |
| rs192756070 | SLC23A3 | tartarate measurement tartronate (hydroxymalonate) measurement X-24432 measurement X-15674 measurement X-16964 measurement |
| rs75032725 | CPNE7 | cysteinylglycine disulfide measurement |
| rs7204478 | ZNF276, FANCA | cysteinylglycine disulfide measurement |
| rs9511186 | C1QTNF9 | cysteinylglycine disulfide measurement |
| rs6496346 | AGBL1 | cysteinylglycine disulfide measurement |
| rs117315039 | COPS8 - COL6A3 | cysteinylglycine disulfide measurement |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”Biological Background
Section titled “Biological Background”Molecular Basis of Detoxification and Redox Balance
Section titled “Molecular Basis of Detoxification and Redox Balance”Cellular function and protection against harmful compounds are critically dependent on robust detoxification pathways and the maintenance of redox balance. Glutathione, a tripeptide containing the cysteinylglycine moiety, is a central biomolecule in these processes. Enzymes belonging to theglutathione S-transferase supergene family catalyze the conjugation of glutathione to a diverse array of electrophilic compounds, thereby facilitating their detoxification and subsequent excretion from the body. [10] This enzymatic action is a key component of cellular defense mechanisms, neutralizing toxins and contributing to the overall stability of the cellular environment.
Genetic Regulation of Metabolic Pathways
Section titled “Genetic Regulation of Metabolic Pathways”Genetic mechanisms exert significant control over the efficiency and specificities of various metabolic processes within the body. Polymorphisms in genes encoding critical enzymes, such as FADS1 (Fatty Acid Desaturase 1), can directly influence the biosynthesis of long-chain polyunsaturated fatty acids from essential precursors like linoleic acid. [8] A reduction in FADS1 catalytic activity or protein abundance, due to genetic variations, can alter the availability of fatty acid intermediates, consequently impacting the synthesis and concentrations of complex glycerophospholipids like phosphatidylcholines and phosphatidylethanolamines. [8] These genetic regulatory elements thus play a crucial role in determining an individual’s unique metabolic profile and the availability of essential biomolecules for cellular structure and signaling.
Genetic Influences on Metabolite Transport and Excretion
Section titled “Genetic Influences on Metabolite Transport and Excretion”Beyond enzymatic activity, genetic variations also modulate the transport and excretion of metabolites, profoundly affecting their circulating concentrations and overall homeostasis. The SLC2A9gene, for example, encodes a urate transporter that significantly influences serum uric acid levels and renal urate excretion.[11] Genetic polymorphisms within SLC2A9can lead to altered transporter function, resulting in homeostatic disruptions such as hyperuricemia, a key factor in the development of gout.[11] These genetic effects on transport mechanisms often exhibit sex-specific patterns, highlighting the intricate interplay between genetic predisposition, cellular function, and systemic physiological regulation. [12]
Systemic Health Implications of Metabolic Dysregulation
Section titled “Systemic Health Implications of Metabolic Dysregulation”Dysregulation within metabolic and detoxification pathways, often stemming from genetic predispositions, can manifest as significant pathophysiological processes affecting multiple tissues and organs. Alterations in lipid metabolism, as influenced by genes like FADS1, can lead to imbalances in glycerophospholipid concentrations, impacting membrane integrity and cellular signaling, which can contribute to broader systemic issues.[8]Similarly, disrupted urate homeostasis due to variants inSLC2A9can precipitate conditions such as gout, involving inflammation and damage in specific joints and potentially the kidneys.[11] Furthermore, genetic polymorphisms in the glutathione S-transferasefamily, by affecting the body’s capacity to detoxify harmful substances, can modulate an individual’s susceptibility to diseases like lung cancer, underscoring the profound and far-reaching systemic consequences of genetic variation on metabolic health.[10]
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Interplay and Redox Homeostasis
Section titled “Metabolic Interplay and Redox Homeostasis”Cysteinylglycine disulfide, as an oxidized dipeptide, is intrinsically linked to cellular redox homeostasis and the broader metabolic pathways of sulfur-containing amino acids. It is considered a metabolite, and its presence in serum is part of the comprehensive metabolic profiles analyzed in genome-wide association studies.[8]The reduced form, cysteinylglycine, is a product of glutathione degradation, a critical tripeptide involved in maintaining the cellular redox state and protecting against oxidative stress. The formation of cysteinylglycine disulfide signifies an oxidative event, reflecting the cellular capacity to manage reactive oxygen species and restore redox balance through reduction-oxidation cycles.
The glutathione S-transferase supergene family, including enzymes like GSTM1 through GSTM5, plays a pivotal role in these metabolic pathways. [6]These enzymes catalyze the conjugation of glutathione to various electrophilic compounds, initiating their detoxification and subsequent breakdown. This process ultimately leads to the generation of cysteinylglycine, which can then be further metabolized or exist in its oxidized disulfide form, thus directly impacting the availability of substrates for redox regulation and detoxification.
Enzymatic Regulation and Detoxification
Section titled “Enzymatic Regulation and Detoxification”The regulation of enzymes involved in glutathione and cysteinylglycine metabolism is crucial for maintaining cellular health and detoxification capacities. Glutathione S-transferases (GSTM) are a prime example, with genetic variants in their gene cluster influencing their activity and the efficiency of detoxification pathways. [6]These enzymes are key players in conjugating endogenous and exogenous toxins with glutathione, facilitating their excretion. The subsequent breakdown of these glutathione conjugates yields cysteinylglycine, implying that the regulation ofGSTMactivity indirectly influences cysteinylglycine and its disulfide form.
Beyond gene regulation, post-translational modifications and allosteric control mechanisms can fine-tune the activity of enzymes throughout the glutathione metabolic pathway. Such regulatory mechanisms ensure that cells can adapt their detoxification and redox responses to varying environmental demands and stress conditions. This dynamic control over enzyme function is essential for the precise management of metabolites like cysteinylglycine disulfide, which serves as an indicator of oxidative burden and metabolic flux.
Systemic Metabolic Integration and Crosstalk
Section titled “Systemic Metabolic Integration and Crosstalk”The metabolism of cysteinylglycine disulfide is not isolated but is integrated within a complex network of systemic metabolic pathways. Metabolomics studies aim to provide a comprehensive readout of the physiological state by measuring endogenous metabolites, revealing how genetic variants associate with the homeostasis of key lipids, carbohydrates, and amino acids.[8]As an amino acid-derived metabolite, cysteinylglycine disulfide’s levels are influenced by and can, in turn, influence, the broader amino acid pool and the cellular redox environment.
Pathway crosstalk is evident as genetic variants affecting other metabolic processes are identified through genome-wide association studies. For instance, genes influencing fatty acid composition, like the FADS1/FADS2 gene cluster, or cholesterol metabolism, such as HMGCR, highlight the interconnectedness of metabolic pathways. [13] Similarly, the SLC2A9gene, involved in uric acid transport, demonstrates how transporters can impact metabolite concentrations in serum, suggesting analogous regulatory influences on other amino acid-related metabolites.[14] This intricate network ensures hierarchical regulation and emergent properties that define an organism’s overall metabolic phenotype.
Clinical Relevance and Disease Implications
Section titled “Clinical Relevance and Disease Implications”Dysregulation of pathways involving glutathione and its metabolites, including cysteinylglycine disulfide, has significant clinical relevance. For example, the glutathione S-transferase supergene family, encompassing genes likeGSTM1-GSTM5, is associated with susceptibility to various diseases, including lung cancer, due to their role in detoxification.[10]Genetic polymorphisms in these genes can lead to altered enzyme activity, impairing the body’s ability to neutralize harmful compounds and manage oxidative stress, which contributes to disease pathogenesis.
Changes in serum metabolite profiles, including those related to amino acids, lipids, and uric acid, are increasingly being recognized as biomarkers for disease risk and progression.[8]Understanding the specific mechanisms by which cysteinylglycine disulfide levels are regulated and how they interact with other metabolic pathways can identify potential therapeutic targets. Modulating these pathways could offer strategies to restore metabolic balance, enhance detoxification, and mitigate oxidative damage in conditions where these processes are compromised.
Clinical Relevance
Section titled “Clinical Relevance”Biomarker for Renal Function and Disease Progression
Section titled “Biomarker for Renal Function and Disease Progression”Cystatin C (cysC) serves as a valuable biomarker for assessing kidney function, offering an alternative to traditional serum creatinine measurements, particularly given concerns about the accuracy of creatinine-based GFR estimation equations in diverse populations or specific clinical presentations. [5] Research indicates that cysC can be used as a continuous trait for kidney function assessment, potentially avoiding errors associated with 24-hour urine collections and the limitations of GFR estimation equations developed in small, selected samples or using specific immunoassay methods. [5]Its utility extends to monitoring disease progression in chronic kidney disease (CKD) and evaluating glomerular filtration rate (GFR) across various clinical scenarios, providing a more robust measure than some traditional methods.[15]
Cardiovascular Risk Assessment and Comorbidities
Section titled “Cardiovascular Risk Assessment and Comorbidities”Beyond its established role in kidney function, cystatin C may also reflect cardiovascular disease risk, suggesting a broader prognostic value in patient care.[5] Studies have explored the implication of the CST3gene, which encodes cystatin C, in the focal progression of coronary artery disease, highlighting a potential link between cystatin C levels and cardiovascular outcomes.[16]This association indicates that cystatin C could be a useful marker for identifying individuals at higher risk for cardiovascular events, even independent of its relationship with kidney function, thereby informing risk assessment and prevention strategies for related conditions and complications.[5]
Genetic Insights and Personalized Risk Stratification
Section titled “Genetic Insights and Personalized Risk Stratification”Genetic studies have identified associations between single nucleotide polymorphisms (SNPs) in or near theCST3 gene and cystatin C levels, offering insights into the genetic determinants of this biomarker. [5] For instance, specific SNPs have shown strong statistical support for association with cysC concentrations. [5]These genetic findings could contribute to personalized medicine approaches by helping to identify individuals with a genetically predisposed higher baseline of cystatin C or a higher risk for conditions where cystatin C plays a role, such as kidney dysfunction or cardiovascular disease. While these findings require replication, they lay groundwork for risk stratification and potentially tailored prevention strategies based on an individual’s genetic profile.[5]
References
Section titled “References”[1] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. 1, 2007, p. 62.
[2] 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. 84, no. 1, 2009, pp. 60-65.
[3] 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. 1, 2007, p. 66.
[4] 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.
[5] 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. S10. PMID: 17903292.
[6] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, p. e1000072. PMID: 18464913.
[7] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 40, no. 12, 2008, pp. 1417–1424.
[8] Gieger, C. et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 4, no. 11, 2008, p. e1000282.
[9] Reiner, Alexander P., et al. “Polymorphisms of the HNF1A gene encoding hepatocyte nuclear factor-1 alpha are associated with C-reactive protein.”The American Journal of Human Genetics, vol. 82, no. 5, 2008, pp. 1193–1201.
[10] Ketterer, B. et al. “The human glutathione S-transferase supergene family, its polymorphism, and its effects on susceptibility to lung cancer.”Environ Health Perspect, vol. 98, 1992, pp. 87–94.
[11] Vitart, V. et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, vol. 40, no. 4, 2008, pp. 437-442.
[12] Doring, A. et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430–436.
[13] Schaeffer, L., et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet 15.10 (2006): 1745–1756.
[14] Do¨ring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.” Nat Genet 40 (2008): 430–436.
[15] Rule, A. D., et al. “Glomerular filtration rate estimated by cystatin C among different clinical presentations.” Kidney International, vol. 69, no. 2, 2006, pp. 399-405.
[16] Eriksson, P., et al. “Human evidence that the cystatin C gene is implicated in focal progression of coronary artery disease.”Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 24, no. 3, 2004, pp. 551-557.