Methionine Sulfoxide
Introduction
Section titled “Introduction”Background and Biological Basis
Section titled “Background and Biological Basis”Methionine sulfoxide is an oxidized derivative of the essential amino acid methionine. As an endogenous metabolite, its concentration can be measured in biological fluids, such as human serum, within the framework of metabolomic studies.[1] Metabolomics is a rapidly advancing field that aims to comprehensively measure all endogenous metabolites in a cell or body fluid, thereby providing a functional readout of an individual’s physiological state. [1]The presence and quantity of methionine sulfoxide reflect specific metabolic processes occurring within the body.
Clinical Relevance
Section titled “Clinical Relevance”Variations in methionine sulfoxide levels, considered part of an individual’s metabolic profile or “metabotype,” can be influenced by genetic factors.[1]Genome-wide association studies (GWAS) investigate the correlation between genetic variants, such as single nucleotide polymorphisms (SNPs), and the concentrations of various metabolites, including methionine sulfoxide, in human serum.[1]These studies suggest that genetically determined metabotypes, which include the levels of metabolites like methionine sulfoxide, may play a role as distinguishing cofactors in the development of common multifactorial diseases.[1]Such genetic influences on amino acid homeostasis can impact an individual’s overall physiological state and their susceptibility to various health conditions.[1]
Social Importance
Section titled “Social Importance”The study of methionine sulfoxide and its genetic determinants carries social importance by enhancing our understanding of human health and disease. Identifying how genetic variations influence metabotypes, including methionine sulfoxide levels, can inform strategies for disease prevention and personalized interventions.[1]Moreover, these metabotypes, in conjunction with environmental factors such as nutrition and lifestyle, can modify an individual’s susceptibility to specific health outcomes.[1] This knowledge can contribute to public health initiatives and guide individuals in making informed choices to improve their well-being.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic association studies exploring traits like methionine sulfoxide often encounter methodological and statistical challenges that can influence the robustness and interpretation of findings. Replication of initial genetic associations remains a significant hurdle, as numerous studies demonstrate that only a subset of reported associations are consistently validated across independent cohorts.[2] This lack of consistent replication may stem from various factors, including the possibility of false positive discoveries in initial reports, differences in statistical power across studies, or variations in experimental design. Furthermore, the necessity for stringent statistical thresholds, such as Bonferroni correction for multiple testing in genome-wide association studies, can inadvertently reduce the power to detect genuine associations, especially those with smaller effect sizes. [3]
The quality and characteristics of the study samples and genetic data also impose limitations on the findings. Many cohorts are composed of volunteers or specific populations, such as twins, which may not fully represent the broader general population, thereby restricting the direct applicability of results. [4] While imputation methods are widely used to infer missing genotypes and enhance genomic coverage, these techniques introduce an estimated error rate, typically ranging from 1.46% to 2.14% per allele, which can subtly affect the accuracy of identified associations. [5]Therefore, meticulous quality control, including the exclusion of single nucleotide polymorphisms (SNPs) with low imputation quality, is essential to mitigate potential biases.[6]
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”A primary limitation in understanding the genetic basis of methionine sulfoxide is the generalizability of research findings. Many studies are predominantly conducted in populations of specific ancestries, such as those of white European descent, which limits the direct transferability of results to other ethnic or racial groups.[7] This is due to potential variations in allele frequencies, linkage disequilibrium patterns, and environmental exposures across diverse populations. Additionally, cohort-specific biases, such as those arising from studies focusing on middle-aged to elderly participants or those collecting DNA at later examination points, can introduce survival bias or restrict the extrapolation of findings to younger demographics. [2]
The precise measurement and consistent definition of methionine sulfoxide levels are crucial for robust genetic studies, yet they present their own set of limitations. Phenotypes derived from the mean of multiple observations or from specific populations like monozygotic twin pairs require careful adjustment to ensure their representativeness of the general population’s variance.[4]Although studies typically account for known covariates such as age, sex, and body mass index, unmeasured environmental factors or complex gene-environment interactions may still confound the observed associations with methionine sulfoxide levels.[8] Furthermore, the removal of extreme phenotypic outliers, while useful for improving statistical models, might inadvertently exclude valuable information regarding genetic effects in individuals at the tails of the distribution. [4]
Unaccounted Variability and Genetic Complexity
Section titled “Unaccounted Variability and Genetic Complexity”Despite advancements in identifying genetic variants, current genome-wide association studies often explain only a fraction of the total heritability for complex traits, a phenomenon often termed “missing heritability.” This suggests that a substantial portion of the genetic influences on methionine sulfoxide levels remains undiscovered, possibly due to the cumulative effect of numerous common variants with individually small effect sizes, rare variants not adequately captured by standard arrays, or intricate epistatic interactions.[4] The limited coverage of current SNP arrays means that some causal genes or genetic variants may be entirely overlooked, particularly if they are not in strong linkage disequilibrium with the genotyped markers. [9]
The precise identification of the causal variants underlying observed associations represents a significant knowledge gap. An associated SNP may merely be a marker in linkage disequilibrium with the true functional variant, rather than the causal variant itself. Different studies might identify distinct SNPs within the same gene or genomic region that are strongly associated with the trait but not with each other, potentially indicating multiple causal variants or population-specific linkage patterns. [10]Moreover, to increase statistical power and mitigate the multiple testing burden, many GWAS employ sex-pooled analyses, which may lead to missing sex-specific genetic associations that could have distinct biological implications for methionine sulfoxide metabolism.[9]
Variants
Section titled “Variants”Genetic variations play a crucial role in shaping individual differences in metabolism, cellular transport, immune response, and overall physiological function. These variations, often single nucleotide polymorphisms (SNPs), can influence gene activity, protein structure, or expression levels, leading to diverse health implications, including those related to oxidative stress and methionine sulfoxide levels.[1]Understanding these genetic associations provides insight into personalized health and disease susceptibility.[11]
Several variants are associated with genes involved in metabolic detoxification and cellular transport. The flavin-containing monooxygenases, including FMO1, FMO2, and FMO4, are critical enzymes that metabolize a wide array of nitrogen-, sulfur-, and phosphorus-containing compounds, encompassing both xenobiotics and endogenous molecules. Variants such asrs141239670 , found in the FMO2 - FMO1 region, and rs714839 within FMO4, may alter the efficiency of these oxidative processes, thereby influencing drug metabolism and the body’s capacity to detoxify harmful substances. [1]These enzymes indirectly contribute to the cellular redox balance, as their activity impacts the overall oxidative burden, which can, in turn, affect the formation and reduction of methionine sulfoxide, a key indicator of oxidative stress.[1]
Other variants affect genes involved in vital cellular transport mechanisms and ion channel function. SLC23A3 (with variant rs192756070 ) is an ascorbate transporter crucial for vitamin C uptake, whileSLC6A19 (linked to rs11133665 in the TERLR1 - SLC6A19region) facilitates amino acid transport, particularly in the kidney and intestine. Similarly,SLC15A4 (associated with rs11613805 in the TMEM132C - SLC15A4 region) transports small peptides, and KCNH6 (variant rs7225568 ) encodes a potassium voltage-gated channel essential for maintaining cellular electrochemical gradients.[12] Variations in these transporter and channel genes can disrupt nutrient absorption, waste excretion, or neuronal signaling, impacting cellular homeostasis. [11]Such disruptions can compromise cellular resilience to oxidative stress, thereby influencing the dynamic balance between methionine and methionine sulfoxide.
Furthermore, variants in genes related to immune response, structural integrity, and gene regulation contribute to individual health profiles. CD4 (variant rs61916275 ) is a co-receptor on T helper cells, indispensable for adaptive immunity, while XYLT1 (part of XYLT1 - RPL7P47 with variant rs8049877 ) initiates the synthesis of proteoglycans, vital components of the extracellular matrix. Other less characterized genes, such as TERLR1, LINC01239 (variant rs842834 ), RPL7P47 (a pseudogene), and TMEM132C, may have regulatory or structural roles that influence cellular processes. [13] Variants in these genes can affect immune cell function, tissue maintenance, or fundamental gene expression patterns. [14]An optimal immune response and cellular integrity are crucial for mitigating oxidative damage, and their dysregulation can directly or indirectly impact the cellular capacity to manage methionine sulfoxide, reflecting broader implications for health.
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 |
| rs141239670 | FMO2 - FMO1 | methionine sulfoxide measurement |
| rs714839 | FMO4 | methionine sulfoxide measurement X-11381 measurement metabolite measurement COVID-19 tumor necrosis factor ligand superfamily member 10 amount |
| rs11133665 | TERLR1 - SLC6A19 | urinary metabolite measurement kynurenine measurement N-acetyl-1-methylhistidine measurement methionine sulfone measurement methionine sulfoxide measurement |
| rs8049877 | XYLT1 - RPL7P47 | methionine sulfoxide measurement |
| rs7225568 | KCNH6 | lipid measurement methionine sulfoxide measurement |
| rs61916275 | CD4 | lipid measurement methionine sulfoxide measurement amino acid measurement isovalerate measurement |
| rs842834 | LINC01239 | methionine sulfoxide measurement |
| rs11613805 | TMEM132C - SLC15A4 | methionine sulfoxide measurement |
Biological Background
Section titled “Biological Background”Methionine sulfoxide is an oxidized form of the amino acid methionine. Research in metabolomics aims to comprehensively measure endogenous metabolites in biological fluids, providing insight into the physiological state of the human body. Genetic variants can influence the homeostasis of key amino acids, and such metabotypes can play a role in the etiology of complex diseases.[1] The identification of genetic variants that alter metabolite homeostasis is crucial for understanding the genetics of complex diseases, and advanced metabolomics platforms, such as electrospray ionization (ESI) tandem mass spectrometry (MS/MS), are employed to profile these metabolites. [1]
Metabolic Processes and Homeostatic Regulation
Section titled “Metabolic Processes and Homeostatic Regulation”Metabolic processes are intricately linked to genetic mechanisms, where specific genetic variants can impact the efficiency of metabolic reactions. For instance, studies have identified polymorphisms associated with different metabolic capacities, affecting pathways like the synthesis of polyunsaturated fatty acids or the beta-oxidation of short- and medium-chain fatty acids. [1] These genetic influences can lead to altered concentrations of substrates and products within metabolic pathways, indicating changes in enzymatic turnover. Such genetically determined metabotypes represent measurable intermediate phenotypes that can reveal the biochemical understanding of common diseases and gene-environment interactions. [1]
Genetic Influence on Cellular Function
Section titled “Genetic Influence on Cellular Function”Genetic mechanisms, including single nucleotide polymorphisms (SNPs), are recognized for their role in altering metabolic phenotypes. These variants can affect gene functions and expression patterns, thereby influencing cellular functions that rely on specific metabolic pathways. For example, changes in gene expression can lead to varying levels of enzymes that process amino acids or other metabolites. The study of these genetic variants in conjunction with metabolomics provides a functional readout of an individual’s physiological state, enabling a deeper understanding of how genotype influences the metabolome and ultimately disease susceptibility.[1]
Systemic Consequences and Pathophysiological Relevance
Section titled “Systemic Consequences and Pathophysiological Relevance”Disruptions in metabolic homeostasis, potentially influenced by genetic variants, can have systemic consequences that contribute to pathophysiological processes. While specific details regarding methionine sulfoxide are not provided in the context, the broader field of metabolomics investigates how genetically determined metabotypes can act as discriminating cofactors in the etiology of common multifactorial diseases.[1] These metabotypes, in interaction with environmental factors, may influence an individual’s susceptibility to certain phenotypes, highlighting the importance of understanding the interplay between genetic makeup and metabolic profiles for personalized health care and nutrition. [1]
References
Section titled “References”[1] Gieger, C et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, 2008.
[2] Benjamin, E. J. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 64.
[3] Gieger, C. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 5, no. 11, 2009, e1000694.
[4] Benyamin, B. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 84, no. 1, 2009, pp. 60–65.
[5] Willer, C. J. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161–169.
[6] Yuan, X. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 5, 2008, pp. 569–581.
[7] Melzer, D. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.
[8] Pare, G. “Novel association of HK1 with glycated hemoglobin in a non-diabetic population: a genome-wide evaluation of 14,618 participants in the Women’s Genome Health Study.”PLoS Genet, vol. 5, no. 12, 2009, e1000729.
[9] Yang, Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 65.
[10] Sabatti, C. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 41, no. 1, 2009, pp. 104-116.
[11] Doring, A et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, 2008.
[12] Vitart, V et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, 2008.
[13] Wilk, JB et al. “Framingham Heart Study genome-wide association: results for pulmonary function measures.” BMC Med Genet, 2007.
[14] Hwang, SJ et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, 2007.