S-Methylcysteine Sulfoxide
Background
Section titled “Background”S-methylcysteine sulfoxide is a naturally occurring sulfur-containing amino acid derivative primarily found in allium vegetables such as garlic, onions, leeks, and chives. It is a non-protein amino acid and is responsible for many of the characteristic flavors and biological activities associated with these plants.
Biological Basis
Section titled “Biological Basis”In allium vegetables, S-methylcysteine sulfoxide serves as a precursor to a variety of bioactive organosulfur compounds. When the plant tissue is damaged (e.g., by cutting or crushing), an enzyme called alliinase (or cysteine sulfoxide lyase) is released. This enzyme acts on S-methylcysteine sulfoxide and other cysteine sulfoxides to produce thiosulfinates, such as allicin from alliin. These thiosulfinates are highly reactive and readily break down into other sulfur compounds, including diallyl sulfides, disulfides, and trisulfides, which are known for their distinctive aromas and biological effects. In the human body, these compounds are metabolized and contribute to the body’s sulfur pool, potentially influencing various metabolic pathways.
Clinical Relevance
Section titled “Clinical Relevance”The organosulfur compounds derived from S-methylcysteine sulfoxide have been extensively studied for their potential health benefits. Research suggests these compounds may possess antioxidant, anti-inflammatory, and antimicrobial properties. They have also been investigated for their potential roles in cardiovascular health, including effects on blood pressure and cholesterol levels, and for their protective effects against certain types of cancer. Individual genetic variations might influence how effectively these compounds are metabolized and, consequently, their impact on health outcomes.
Social Importance
Section titled “Social Importance”Allium vegetables, rich in S-methylcysteine sulfoxide, are fundamental ingredients in cuisines worldwide, contributing significantly to flavor profiles and culinary traditions. Beyond their gastronomic appeal, the perceived health benefits associated with these compounds have led to their use in traditional medicine and the development of dietary supplements. Understanding the biological basis and clinical relevance of S-methylcysteine sulfoxide helps illuminate the nutritional and health implications of consuming these common dietary components, making it relevant for public health and dietary guidance.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genome-wide association studies (GWAS) are inherently susceptible to various methodological and statistical limitations that can influence the robustness and interpretation of findings. Many studies face challenges related to sample size, which can lead to inadequate statistical power and an increased risk of false negative findings, where true associations remain undetected.[1]Conversely, small sample sizes or specific study designs might contribute to effect-size inflation, exaggerating the perceived impact of identified genetic variants. The reliance on imputation analyses, often based on reference panels like HapMap, introduces variability; the quality of imputation (e.g., as indicated by R-squared estimates) can affect the confidence in imputed single nucleotide polymorphisms (SNPs) and, consequently, the reliability of associations.[2]
Furthermore, the extensive number of statistical tests performed in GWAS necessitates stringent correction for multiple comparisons, such as Bonferroni correction, to control the genome-wide significance level. This conservative approach, while reducing false positives, may inadvertently mask true associations, particularly for variants with modest effects or those linked to complex phenotypes. [3] Replication across independent cohorts is crucial for validating initial discoveries, yet many reported associations do not consistently replicate across studies, potentially due to false positive findings in initial screens, differing study designs, or variations in key modifying factors between cohorts. [1] Apparent non-replication at the SNP level can also occur if different studies identify distinct but strongly linked SNPs or multiple causal variants within the same gene, highlighting the complexity of genetic architecture. [4] Meta-analyses, while powerful, also require careful assessment of heterogeneity among contributing studies to ensure combined estimates are valid. [2]
Generalizability and Phenotype Assessment
Section titled “Generalizability and Phenotype Assessment”The utility and broader applicability of genetic findings are often constrained by the demographic characteristics of the study populations. Many large-scale GWAS cohorts predominantly comprise individuals of white European ancestry, often within specific age ranges (e.g., middle-aged to elderly). [1] This limited ethnic diversity makes it challenging to generalize results to younger populations or individuals from other ethnic or racial backgrounds, where genetic architectures and environmental exposures may differ significantly. [1] Such population stratification can lead to biased association findings if not adequately addressed.
Phenotype definition and measurement also present significant limitations. The characterization of complex traits can be challenging, particularly when relying on indirect markers or aggregated data. For instance, using a single marker (e.g., cystatin C for kidney function or TSH for thyroid function) may not fully capture the underlying biological process and could reflect other health conditions.[5] Averaging phenotype measurements over long periods, as sometimes done to reduce regression dilution bias, may introduce misclassification due to evolving diagnostic equipment or mask age-dependent genetic and environmental effects on the trait. [6] Additionally, choices in statistical modeling, such as focusing exclusively on multivariable-adjusted models, could lead to overlooking important bivariate associations between genetic variants and specific phenotypes. [5]
Unexplained Variance and Future Research Directions
Section titled “Unexplained Variance and Future Research Directions”Despite advancements in GWAS, a substantial portion of the heritability for complex traits remains unexplained, a phenomenon often referred to as “missing heritability.” Current genotyping arrays, even those considered genome-wide, may not capture all functional genetic variation, particularly less common variants, structural variations, or those located in regions not well covered by the array. [7] This incomplete coverage means that some causal genes or pathways may be entirely missed, limiting a comprehensive understanding of a trait’s genetic underpinnings. [8]
Moreover, the observed genetic associations represent only a part of the complex interplay determining a phenotype. The influence of environmental factors, gene-environment interactions, and epigenetic modifications are often not fully accounted for in standard GWAS designs. [6] These unmeasured factors can confound or modify genetic effects, making it difficult to fully elucidate the etiology of a trait. A fundamental challenge that remains is the prioritization and functional validation of identified genetic variants. While statistical significance highlights associations, the precise biological mechanisms by which these variants influence the trait often require extensive follow-up studies, including functional genomics and mechanistic investigations, to translate statistical correlations into biological insights. [1]
Variants
Section titled “Variants”Genetic variations can profoundly influence an individual’s metabolism, affecting the levels of circulating compounds like s methylcysteine sulfoxide. Among these, the variant rs3754487 located in the FMO3 gene is particularly notable. The FMO3 gene encodes flavin-containing monooxygenase 3, a liver enzyme essential for detoxifying a wide range of nitrogen- and sulfur-containing compounds, including dietary components and xenobiotics. Variations in FMO3 can alter its enzymatic activity, thereby impacting the body’s ability to process various metabolites and potentially influencing s methylcysteine sulfoxide levels through indirect metabolic routes. [3] Further, non-coding regions such as those associated with rs2825632 in the RNU1-139P - RPL37P4 locus and rs6843352 within the FTH1P21 - LINC02272 region may play regulatory roles. While RNU1-139P and RPL37P4 are pseudogenes and FTH1P21is a ferritin heavy chain pseudogene, they can still contribute to gene expression regulation or indicate functional elements within the genome that could modulate cellular processes and overall metabolic health.[9] The long intergenic non-coding RNA LINC02272 may also exert regulatory functions, influencing pathways relevant to circulating metabolite concentrations.
Other variants highlight the broad genetic landscape affecting human physiology and metabolism. The rs78239230 variant in PRANCR (Prostate Adenocarcinoma Noncoding RNA) and rs2739819 in SNHG14(Small Nucleolar RNA Host Gene 14) are located within long non-coding RNA (lncRNA) genes. LncRNAs are crucial regulators of gene expression, affecting processes from cellular development to disease pathology, and variations in these regions can alter gene regulatory networks, which in turn might impact metabolic pathways and the processing of sulfur compounds.[5] Additionally, the rs139427838 variant in the EPB41L3 gene, encoding a cytoskeletal protein, and rs11254630 in the ST8SIA6 - PRPF38AP2 region, which includes a sialyltransferase gene, could influence cellular architecture, cell signaling, or the biosynthesis of complex carbohydrates. Such fundamental cellular alterations can have cascading effects on metabolic homeostasis and the body’s response to various dietary or environmental factors. [2]
Variants in genes involved in cellular communication, immune response, and fundamental cellular machinery also contribute to the complex interplay of genetics and metabolism. For instance, rs9946127 in DLGAP1 (DLG Associated Protein 1) is related to synaptic organization and neuronal signaling, suggesting potential indirect effects on metabolic regulation through neurological pathways. The rs145925298 variant in PIBF1 (Progesterone Induced Blocking Factor 1) is associated with immune regulation, and changes here could impact inflammatory processes or hormonal balances, which are intertwined with metabolic health. [1] Finally, the rs2815154 variant spans a complex genomic region involving EEF1E1-BLOC1S5, BLOC1S5-TXNDC5, and BLOC1S5. These genes are implicated in protein synthesis (EEF1E1), lysosomal biogenesis (BLOC1S5), and cellular redox regulation (TXNDC5). Variations affecting these basic cellular functions can collectively influence the efficiency of metabolic processes, including those that might produce or modify s methylcysteine sulfoxide, underscoring the polygenic nature of metabolite levels. [10]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs3754487 | MROH9 - FMO3 | S-methylcysteine sulfoxide measurement |
| rs2825632 | RNU1-139P - RPL37P4 | S-methylcysteine sulfoxide measurement |
| rs6843352 | FTH1P21 - LINC02272 | S-methylcysteine sulfoxide measurement |
| rs78239230 | PRANCR | S-methylcysteine sulfoxide measurement |
| rs139427838 | EPB41L3 | S-methylcysteine sulfoxide measurement |
| rs11254630 | ST8SIA6 - PRPF38AP2 | S-methylcysteine sulfoxide measurement |
| rs2739819 | SNHG14 | S-methylcysteine sulfoxide measurement |
| rs9946127 | DLGAP1 | S-methylcysteine sulfoxide measurement memory performance |
| rs145925298 | PIBF1 | S-methylcysteine sulfoxide measurement |
| rs2815154 | EEF1E1-BLOC1S5, BLOC1S5-TXNDC5, BLOC1S5 | S-methylcysteine sulfoxide measurement |
Biological Background
Section titled “Biological Background”Pathways and Mechanisms
Section titled “Pathways and Mechanisms”References
Section titled “References”[1] Benjamin, E. J. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, no. 1, Sept. 2007, 70. PMID: 17903293.
[2] Yuan, X. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 4, Oct. 2008, pp. 520-528. PMID: 18940312.
[3] Gieger, C. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 4, no. 11, Nov. 2008, e1000282. PMID: 19043545.
[4] Sabatti, C. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 40, no. 12, Dec. 2008, pp. 1394-1402. PMID: 19060910.
[5] Hwang, S. J. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, no. 1, Sept. 2007, 69. PMID: 17903292.
[6] Vasan, R. S. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. 1, Sept. 2007, 73. PMID: 17903301.
[7] Yang, Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, no. 1, Sept. 2007, 68. PMID: 17903294.
[8] O’Donnell, C. J. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet, vol. 8, no. 1, Sept. 2007, 72. PMID: 17903303.
[9] Melzer, D. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 4, Apr. 2008, e1000072. PMID: 18464913.
[10] Kathiresan, S. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 12, Dec. 2008, pp. 1421-1428. PMID: 19060906.