Cysteine S Sulfate
Cysteine S sulfate is a naturally occurring sulfur-containing metabolite, specifically a derivative of the amino acid cysteine. It plays a role in the complex network of sulfur metabolism within the human body. Understanding its physiological levels and metabolic pathways is important for assessing overall metabolic health.
Biological Basis
Section titled “Biological Basis”Cysteine is a semi-essential amino acid critical for protein synthesis, detoxification, and various metabolic processes. Cysteine S sulfate is formed as an intermediate or end-product during the catabolism of cysteine. This metabolic pathway is part of the body’s mechanism for handling excess sulfur-containing compounds and maintaining cellular redox balance. The concentration of cysteine S sulfate in biological fluids can reflect the efficiency of these metabolic processes and the status of sulfur amino acid metabolism.
Clinical Relevance
Section titled “Clinical Relevance”As a metabolite, cysteine S sulfate can serve as a potential biomarker for various physiological states and metabolic disorders. Imbalances in sulfur amino acid metabolism are associated with conditions such as oxidative stress, renal dysfunction, and certain inherited metabolic diseases. Monitoring cysteine S sulfate levels may offer insights into these underlying metabolic disturbances, potentially aiding in diagnosis or disease management.
Social Importance
Section titled “Social Importance”The study of metabolites like cysteine S sulfate contributes to the broader field of metabolomics, which seeks to comprehensively understand the chemical processes involving metabolites. By identifying and quantifying such molecules, researchers can gain a deeper understanding of human health, disease mechanisms, and the impact of environmental factors. This knowledge can ultimately lead to the development of new diagnostic tools, therapeutic strategies, and personalized medicine approaches, improving public health outcomes by providing a more detailed picture of an individual’s metabolic profile.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Several studies acknowledge that their moderate cohort sizes contribute to a susceptibility to false negative findings due to inadequate statistical power. [1] The ultimate validation of findings often requires replication in other cohorts, as many initial p-values may represent false positive associations in the absence of external validation. [2] Indeed, a significant challenge in genome-wide association studies (GWAS) is the need to sort through associations and prioritize SNPs for follow-up, with replication being a fundamental step to confirm true positive genetic associations. [1]
The use of 100K SNP arrays in some studies means that coverage of certain gene regions might be insufficient to exclude real associations, suggesting that more dense SNP arrays could reveal additional variants. [3] Furthermore, some reported p-values were unadjusted for multiple comparisons, implying that many associations may not meet stringent genome-wide significance thresholds after correction. [4] The estimation of effect sizes can also be complex; for instance, effects expressed in standard deviations of mean phenotypes may need scaling to reflect the proportion of phenotypic variance explained in the wider population. [4] Additionally, choices in analytical models, such as a focus on multivariable analyses, may lead to missing important bivariate associations. [2]
Phenotypic Ascertainment and Confounders
Section titled “Phenotypic Ascertainment and Confounders”The definition and measurement of certain traits present limitations. For kidney function, reliance on a single serum creatinine measure could lead to misclassification, and the use of equations like MDRD, known to underestimate GFR in healthy individuals, may introduce additional misclassification. [2] Similarly, while spot urine specimens for urinary albumin excretion (UAE) approximate 24-hour collections, they are not the gold standard. [2]The selection of cystatin C (cysC) as a kidney function marker also carries the caveat that it may reflect cardiovascular disease risk independently of kidney function.[2]
The research also highlights potential confounding influences on biomarker levels. For example, variations in serum markers for iron status are known to be affected by the time of day blood is collected and menopausal status. [4]In some cases, the use of surrogate markers, such as TSH for thyroid function, is necessitated by the absence of more direct measures like free thyroxine or a reliable assessment of thyroid disease within the study sample.[2] Even when efforts are made to adjust for confounders, the initial trait definition and measurement methods remain a limitation.
Generalizability and Cohort Specificity
Section titled “Generalizability and Cohort Specificity”A significant limitation across several studies is the demographic composition of the cohorts, which are largely comprised of individuals of white European descent. [5] This lack of ethnic diversity means that the findings may not be generalizable to younger individuals or those from other ethnic or racial backgrounds, making it uncertain how results would apply to broader populations. [2]
The specific characteristics of the study populations can also limit generalizability. For instance, cohorts that are predominantly middle-aged to elderly, or where DNA collection occurs at later examinations, may introduce a survival bias, potentially skewing the observed associations. [1]Furthermore, studies not specifically selected for particular conditions, such as severe chronic kidney disease (CKD), may have participants with only moderate forms of the condition, which could impact the applicability of findings to the full spectrum of the disease.[2]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing a wide array of biological processes, including the metabolism of cysteine and sulfate, which are vital for cellular health and detoxification. The_MOCS1_gene, for instance, is fundamental for synthesizing the molybdenum cofactor (MoCo), a necessary component for sulfite oxidase. This enzyme is critical for converting toxic sulfite, a byproduct of cysteine breakdown, into harmless sulfate. Variants such asrs11964984 and rs78643010 within or near _MOCS1_, and the intergenic variant rs75773116 located in the _MOCS1 - RPL23P6_ region, may affect the efficiency of MoCo synthesis or _MOCS1_ expression, thereby impacting the body’s ability to process sulfur compounds and maintain appropriate sulfate levels. [6] Similarly, the _HS3ST4_ gene (Heparan Sulfate 3-O-Sulfotransferase 4) is directly involved in the sulfation of heparan sulfate, a complex sugar molecule important for cell signaling and tissue structure. The variant rs4787377 , near _HS3ST4_ and _LINC02195_, could influence the activity of this sulfotransferase, potentially altering the production of sulfated compounds and affecting related biological pathways. [6]
Other variants influence general cellular transport, mitochondrial function, and lysosomal activity, indirectly affecting cysteine and sulfate metabolism. The_SLC23A3_gene encodes a transporter for L-ascorbic acid (Vitamin C), an essential antioxidant involved in numerous metabolic reactions. The variantrs192756070 in _SLC23A3_may alter Vitamin C transport, which could impact overall redox balance and cellular pathways that interact with sulfur metabolism.[6] The _VPS33B_ gene (Vacuolar Protein Sorting 33 Homolog B) is vital for intracellular membrane trafficking and lysosomal function, processes critical for cellular waste removal and nutrient recycling. The variant rs11857433 , associated with _VPS33B-DT, VPS33B_, might affect these crucial cellular maintenance systems, leading to broader metabolic disruptions. Furthermore, the rs1586404 variant located near _MTCYBP41_ (Mitochondrial Cytochrome B Processing 41) and a _Y_RNA_ may influence mitochondrial function, as _MTCYBP41_ is involved in maintaining mitochondrial integrity and energy production. Compromised mitochondrial health can have systemic effects on metabolism, including the intricate pathways of sulfur-containing amino acids. [6]
Variants in genes related to cell signaling, neural development, or less characterized functions also contribute to the complex interplay of genetic factors. The _PTPN6_ gene (Protein Tyrosine Phosphatase Non-Receptor Type 6), also known as SHP-1, plays a significant role in immune cell signaling and inflammatory responses. The variant rs185835888 could modulate immune system activity, which in turn can influence systemic metabolic states and the body’s handling of various compounds, including cysteine and sulfate.[6] Genes like _TENM4_ and _TENM2_ (Teneurin Transmembrane Proteins 4 and 2) are involved in neuronal development and cell adhesion. The variants rs7121373 (in _TENM4 - RNU6-544P_) and rs190171031 (in _TENM2_) may affect these developmental processes, potentially having indirect impacts on neurological and systemic metabolic regulation. Lastly, the _MROH1_ gene (MROH Domain Containing 1) with variant rs910978174 is less fully understood but is broadly implicated in fundamental cellular processes, and variations here could contribute to subtle shifts in cellular function that collectively impact overall metabolic health and the efficiency of sulfur compound processing. [6]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs192756070 | SLC23A3 | tartarate measurement tartronate (hydroxymalonate) measurement X-24432 measurement X-15674 measurement X-16964 measurement |
| rs11964984 rs78643010 | MOCS1 | cysteine s-sulfate measurement |
| rs75773116 | MOCS1 - RPL23P6 | cysteine s-sulfate measurement |
| rs185835888 | PTPN6 | cysteine s-sulfate measurement |
| rs7121373 | TENM4 - RNU6-544P | cysteine s-sulfate measurement |
| rs1586404 | MTCYBP41 - Y_RNA | cysteine s-sulfate measurement |
| rs11857433 | VPS33B-DT, VPS33B | cysteine s-sulfate measurement |
| rs4787377 | HS3ST4 - LINC02195 | cysteine s-sulfate measurement |
| rs190171031 | TENM2 | cysteine s-sulfate measurement |
| rs910978174 | MROH1 | cysteine s-sulfate measurement |
Causes of Cysteine s Sulfate (cysC) Levels
Section titled “Causes of Cysteine s Sulfate (cysC) Levels”The concentration of cysteine s sulfate (cysC) in the body is influenced by a combination of genetic factors and physiological states, reflecting both inherited predispositions and the functional status of various organ systems. Research, particularly through genome-wide association studies, has elucidated key contributors to the variability in cysC levels.
Genetic Predisposition
Section titled “Genetic Predisposition”Inherited genetic variations are a primary determinant of circulating cysteine s sulfate (cysC) levels. Genome-wide association studies (GWAS) have identified specific single nucleotide polymorphisms (SNPs) that are significantly associated with cysC concentration. Notably, four SNPs found in or near theCST3 gene showed strong statistical associations with cysC levels, with p-values ranging from 8.5x10^-09 to 0.007. [2]
These genetic variants contribute to the observed inter-individual variability in cysC levels. For example, a specific SNP, rs1158167 , is reported to account for 2.5% of the total variation in cysC within the studied population. [2] The discovery of such variants, analyzed using models like generalized estimating equations (GEE) and family-based association tests (FBAT) with an additive allele assumption, underscores the direct genetic influence on the biological pathways regulating cysC production or metabolism. [2]
Physiological and Comorbid Conditions
Section titled “Physiological and Comorbid Conditions”Beyond genetic factors, the physiological state of an individual, particularly kidney function, is a primary determinant of cysteine s sulfate (cysC) levels. CysC is widely recognized as a reliable marker of kidney function[2]implying that its concentration in the blood directly reflects the kidneys’ filtration efficiency. Therefore, conditions that impair kidney function, such as chronic kidney disease, will lead to elevated cysC levels due to reduced renal clearance.
Furthermore, cysC levels may also serve as an indicator for cardiovascular disease risk, even independent of its relationship with kidney function.[2]This suggests that underlying cardiovascular pathologies or the presence of various cardiovascular risk factors can contribute to altered cysC concentrations. While the exact mechanisms are complex, these physiological and comorbid conditions underscore the systemic influences that modulate cysC levels, reflecting overall health and disease status.[2]
Biological Background
Section titled “Biological Background”Renal Physiology and Biomarkers
Section titled “Renal Physiology and Biomarkers”The kidney plays a critical role in maintaining bodily homeostasis, and its function is often assessed by specific biomarkers. Cystatin C (cysC), a proteinase inhibitor, serves as an important marker for kidney function, offering a reliable estimate of glomerular filtration rate (GFR) . Unlike creatinine, cystatin C levels are less influenced by factors such as muscle mass, making it a valuable tool for assessing renal health across diverse populations[7]. [8] Variations in the CST3gene, which encodes cystatin C, have been highly associated with circulating cysC levels, with specific single nucleotide polymorphisms (SNPs) likers1158167 showing strong associations .
Beyond cystatin C, other molecules are crucial for renal function and overall metabolic balance. Uric acid, a byproduct of purine metabolism, is primarily excreted by the kidneys, and its serum levels are influenced by genetic factors.[9] The SLC2A9gene, encoding a urate transporter, significantly impacts serum uric acid concentrations and excretion, with variations in this gene linked to conditions like gout[10]. [11]These genetic and molecular mechanisms underscore the intricate regulatory networks governing renal physiology and the utility of biomarkers like cystatin C and uric acid in clinical assessment.
Protein Function and Metabolic Regulation
Section titled “Protein Function and Metabolic Regulation”Proteins play diverse roles in cellular function, including inhibition of proteolytic enzymes and detoxification processes. Cystatin C functions as a potent inhibitor of cysteine proteases, enzymes involved in protein degradation . This inhibitory activity is crucial for regulating various cellular processes and maintaining protein homeostasis. The systemic levels of cystatin C are thus not only indicative of kidney function but also reflect its broader involvement in proteolytic balance throughout the body.
Another important family of enzymes involved in metabolic regulation and detoxification are the glutathione S-transferases (GSTs). These enzymes catalyze the conjugation of glutathione, a tripeptide containing cysteine, to a wide range of electrophilic compounds, facilitating their removal from the body.[12]The human glutathione S-transferase supergene family exhibits polymorphism, which can influence an individual’s capacity for detoxification. This highlights the importance of cysteine-containing molecules in cellular defense mechanisms and metabolic pathways.
Systemic Health Interconnections
Section titled “Systemic Health Interconnections”Kidney function and the associated biomarkers like cystatin C are intricately linked to broader systemic health, including cardiovascular and endocrine systems. Elevated cystatin C levels, indicative of impaired kidney function, have been associated with an increased risk of cardiovascular disease . Furthermore, theCST3gene itself, which codes for cystatin C, has been implicated in the focal progression of coronary artery disease, suggesting a direct role for this protein beyond its utility as a renal marker.[13]
Endocrine traits also exhibit complex interconnections with overall health. Thyroid stimulating hormone (TSH) serves as a key indicator of thyroid function, which can influence various metabolic parameters . For instance, thyroid dysfunction has been linked to altered cholesterol levels in older populations.[14] Beyond circulating proteins and hormones, structural components like chondroitin sulfate proteoglycans, such as Neurocan in the brain, also contribute to tissue integrity and function. [15]These multifaceted interactions underscore how a single biomarker or genetic variation can have far-reaching systemic consequences, affecting multiple organs and disease pathways.
Clinical Relevance of Cysteine S Sulfate
Section titled “Clinical Relevance of Cysteine S Sulfate”Cystatin C: A Multifaceted Biomarker for Renal Function and Cardiovascular Risk
Section titled “Cystatin C: A Multifaceted Biomarker for Renal Function and Cardiovascular Risk”Cystatin C (cysC) serves as a valuable clinical biomarker, primarily recognized for its role in assessing kidney function. [2] Unlike traditional methods, cysC can be used as a continuous trait, circumventing the limitations of transforming equations that may be derived from small, selected populations or different measurement techniques. [2] Its precise measurement through particle-enhanced immunonephelometry, with low inter- and intra-assay variability, underscores its potential for reliable diagnostic utility and monitoring strategies in patient care. [2]
Beyond its established utility in renal assessment, research suggests that cysC may also independently reflect cardiovascular disease risk, offering insights above and beyond its association with kidney function.[2]This dual implication positions cysC as a promising marker for risk assessment, potentially aiding in identifying individuals at elevated risk for both renal impairment and cardiovascular complications. Such comprehensive utility could inform more holistic monitoring strategies and guide early interventions to mitigate associated comorbidities. Comparisons have been made between cysC, plasma creatinine, and the Cockcroft and Gault formula for estimating glomerular filtration rate, with cysC offering advantages in certain clinical presentations.[8]
Genetic Influences on Cystatin C Levels and Their Clinical Utility
Section titled “Genetic Influences on Cystatin C Levels and Their Clinical Utility”Genetic research has identified specific single nucleotide polymorphisms (SNPs) in or near theCST3 gene that are significantly associated with varying cysC levels, such as rs1158167 and rs563754 . [2] These genetic associations provide a foundation for understanding the hereditary components influencing cysC concentrations, which could be critical for personalized medicine approaches. By identifying individuals with genetic predispositions to altered cysC levels, clinicians might refine risk stratification for conditions where cysC plays a role.
The identification of these genetic loci holds promise for future risk stratification strategies, allowing for the proactive identification of high-risk individuals who might benefit from targeted prevention or early intervention. While current findings from population-based cohorts like the Framingham Heart Study require replication in diverse populations, they highlight the potential for integrating genetic information into comprehensive patient assessments. [2] This could eventually lead to more precise diagnostic and prognostic tools, tailoring clinical management to an individual’s unique genetic profile.
Prognostic Value in Disease Progression and Outcomes
Section titled “Prognostic Value in Disease Progression and Outcomes”The levels of cystatin C offer significant prognostic value, extending to the prediction of disease progression and long-term clinical outcomes, particularly concerning cardiovascular health. Human evidence indicates that variations within the cystatin C gene are implicated in the focal progression of coronary artery disease.[13] This suggests that cysC is not merely a marker of kidney function but also a potential indicator of vascular pathology.
Consequently, monitoring cysC levels could serve as a valuable tool for predicting the trajectory of cardiovascular disease and its complications, allowing for earlier and more aggressive management strategies. While the generalizability of some findings from studies like the Framingham Heart Study, which primarily involved a non-ethnically diverse cohort, needs further validation, the consistent association of cysC with both renal and cardiovascular health underscores its potential as a broad prognostic indicator.[2] This biomarker’s ability to reflect risk beyond kidney function offers a more comprehensive view of patient prognosis.
References
Section titled “References”[1] Benjamin, E. J. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007.
[2] Hwang, S. J. “A Genome-Wide Association for Kidney Function and Endocrine-Related Traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S10.
[3] O’Donnell, C. J. “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.
[4] Benyamin, B. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 83, no. 6, 2008.
[5] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008.
[6] Willer, C. J. et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nature Genetics, vol. 40, no. 2, 2008.
[7] 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.
[8] Hoek, F. J., et al. “A Comparison Between Cystatin C, Plasma Creatinine and the Cockcroft and Gault Formula for the Estimation of Glomerular Filtration Rate.” Nephrology Dialysis Transplantation, vol. 18, no. 10, 2003, pp. 2024-2031.
[9] Yang, Q., et al. “Genome-Wide Search for Genes Affecting Serum Uric Acid Levels: The Framingham Heart Study.”Metabolism, vol. 54, no. 11, 2005, pp. 1435-1441.
[10] Vitart, V., et al. “SLC2A9 Is a Newly Identified Urate Transporter Influencing Serum Urate Concentration, Urate Excretion and Gout.”Nature Genetics, vol. 40, no. 4, 2008, pp. 437-442.
[11] Do¨ring, A., et al. “SLC2A9 Influences Uric Acid Concentrations with Pronounced Sex-Specific Effects.”Nature Genetics, vol. 40, no. 4, 2008, pp. 430-436.
[12] Ketterer, B., et al. “The Human Glutathione S-Transferase Supergene Family, Its Polymorphism, and Its Effects on Susceptibility to Lung Cancer.”Environmental Health Perspectives, vol. 98, 1992, pp. 87-94.
[13] Eriksson, P., et al. “Human evidence that the cystatin C gene is implicated in focal progression of coronary artery disease.”Arterioscler Thromb Vasc Biol, vol. 24, no. 3, 2004, pp. 551-557.
[14] Kanaya, A. M., et al. “Association Between Thyroid Dysfunction and Total Cholesterol Level in an Older Biracial Population: The Health, Aging and Body Composition Study.”Archives of Internal Medicine, vol. 162, no. 7, 2002, pp. 773-779.
[15] Rauch, U., et al. “Neurocan: A Brain Chondroitin Sulfate Proteoglycan.” Cellular and Molecular Life Sciences CMLS, vol. 58, no. 13, 2001, pp. 1842-1856.