Methionine
Introduction
Section titled “Introduction”Methionine is a sulfur-containing amino acid that plays a fundamental role in human metabolism. It is central to protein synthesis and serves as a precursor for various crucial biological compounds. Its metabolic pathways are intricately linked to cellular methylation processes and the synthesis of other important amino acids.[1]The rapidly evolving field of metabolomics, which aims at comprehensive measurement of endogenous metabolites, facilitates the study of genetic variants and their impact on amino acid homeostasis.[1]
Biological Basis
Section titled “Biological Basis”As a key metabolite, methionine is involved in one-carbon metabolism, primarily through its conversion to S-adenosylmethionine (SAMe), which acts as a universal methyl donor in numerous biochemical reactions. These methylation reactions are vital for processes such as DNA methylation, neurotransmitter synthesis, and hormone metabolism.[1]Methionine metabolism is also linked to homocysteine, an intermediate that can be recycled back to methionine or converted to cysteine, a precursor for glutathione synthesis. Variations in genes affecting these metabolic pathways can lead to altered methionine and related metabolite levels in serum, forming “genetically determined metabotypes”.[1] Studies like genome-wide association studies (GWAS) with metabolomics are designed to identify such genetic variants that alter the homeostasis of key metabolites, including amino acids.[1]
Clinical Relevance
Section titled “Clinical Relevance”Genetic variants associated with changes in the homeostasis of amino acids like methionine are expected to provide a functional understanding of the genetics of complex diseases.[1]Dysregulation of methionine metabolism, potentially influenced by single nucleotide polymorphisms (SNPs), can lead to altered levels of metabolites. For instance, the impact of polymorphisms on metabolic pathways, such as those involving amino acid interconversion, can be observed through changes in metabolite concentrations.[1]Identifying these genetically determined metabotypes through GWAS provides critical insights into disease pathogenesis and gene-environment interactions.[1]
Social Importance
Section titled “Social Importance”Understanding the genetic influences on methionine metabolism, as revealed by studies combining genomics and metabolomics, can contribute to the development of personalized health care and nutrition strategies.[1] By identifying “genetically determined metabotypes” related to amino acids, it becomes possible to move towards individualized medication and nutrition based on a combination of genotyping and metabolic characterization.[1] This approach aims to functionally investigate the role of gene-environment interactions in the etiology of complex diseases.[1]
Generalizability and Phenotypic Measurement Challenges
Section titled “Generalizability and Phenotypic Measurement Challenges”The interpretation of genetic associations with methionine is subject to limitations concerning the generalizability of study populations and the precision of phenotypic measurements. Many studies utilize cohorts with specific characteristics, such as volunteer participants, twin populations, or individuals of a particular age range or ancestry, which may limit the applicability of findings to the broader general population.[2] For instance, studies predominantly involving individuals of white European ancestry may not fully capture genetic influences or their effect sizes in other ethnic or racial groups, impacting the universality of identified variants.[3] Furthermore, the selection processes, such as the exclusion of individuals on certain medications or the collection of DNA at later examinations, can introduce survival or cohort biases that might skew results.[3]Phenotypic measurement itself presents challenges that can influence the robustness of genetic associations with methionine. Factors like the time of day blood samples are collected or an individual’s menopausal status are known to affect various serum markers, necessitating careful standardization and adjustment for covariates.[2] When phenotypes are derived from means of repeated observations or from monozygotic twin pairs, appropriate statistical adjustments are critical to accurately estimate effect sizes and the proportion of variance explained in the wider population.[2] Additionally, the process of imputing missing genotypes can introduce minor error rates, typically between 1.5% and 2.1%, which may subtly affect the accuracy of identified associations.[4]
Statistical Power and Replication Limitations
Section titled “Statistical Power and Replication Limitations”Studies investigating the genetic basis of methionine are often constrained by statistical power and the inherent challenges of replicating findings. Moderate cohort sizes can lead to a susceptibility for false negative findings, meaning true genetic associations might be missed due to insufficient statistical power.[3] Conversely, initial discoveries, particularly those with larger reported effect sizes, may sometimes represent inflated estimates, and their replication in independent cohorts is crucial for validation.[5] The replication of specific genetic associations, defined as identifying the same SNP or one in strong linkage disequilibrium with the same direction of effect, has historically been inconsistent across studies, with some meta-analyses showing replication for only about a third of examined associations.[3] Discrepancies in replication can arise from various factors, including false positive results in initial studies, differences in key factors between study cohorts, inadequate statistical power in replication attempts, or the presence of multiple causal variants within the same gene that are not in strong linkage disequilibrium with each other across populations.[3]
Incomplete Genetic Architecture and Environmental Factors
Section titled “Incomplete Genetic Architecture and Environmental Factors”Understanding the complete genetic architecture underlying methionine levels is a significant challenge, as current research often explains only a fraction of the total phenotypic variance. For example, while specific variants may explain a notable percentage of variation for some traits, a substantial portion often remains unexplained, highlighting the concept of “missing heritability”.[2] Genome-wide association studies (GWAS) typically focus on common genetic variants and are limited by the coverage of available SNP arrays, potentially missing associations with rarer variants or those not well-represented in reference panels like HapMap.[6]This limited coverage means that GWAS data alone may not be sufficient for a comprehensive study of candidate genes or for uncovering complex genetic mechanisms influencing methionine metabolism.[6]Beyond genetics, environmental and physiological factors play a crucial role and can confound genetic associations with methionine. While efforts are made to adjust for known covariates such as age, sex, and other health indicators, the intricate interplay of environmental exposures and gene-environment interactions is often difficult to fully capture.[7]The ultimate validation of genetic findings for methionine will require not only replication in diverse cohorts but also functional studies to elucidate the biological mechanisms by which identified genetic variants influence methionine pathways.[3] The reliance on accurate estimates of phenotypic variance and heritability for calculating the proportion of genetic variance explained by SNPs underscores the importance of robust initial population-level characterization.[2]
Variants
Section titled “Variants”Genetic variations play a crucial role in influencing a wide array of biological processes, from metabolism and immune response to cellular signaling and gene expression. These single nucleotide polymorphisms (SNPs) can reside within or near genes, potentially altering their function or expression levels, and thereby impacting complex traits such as those related to methionine metabolism. Methionine is an essential amino acid, fundamental for protein synthesis, cellular methylation cycles, and the production of crucial sulfur-containing compounds vital for antioxidant defense.
Variants near genes involved in metabolic processing and immune signaling can have broad systemic effects. For instance, the FMO4 (Flavin-containing monooxygenase 4) gene encodes an enzyme primarily responsible for metabolizing a diverse range of xenobiotics and endogenous compounds, particularly those containing sulfur. Genetic variations in FMO4, such as rs10912810 , could modify its enzymatic activity, potentially affecting the processing of sulfur-containing molecules that are closely linked to the methionine metabolic pathway. TheCD14 gene, on the other hand, encodes a receptor protein predominantly found on innate immune cells, playing a critical role in recognizing bacterial components and triggering inflammatory responses. A variant like rs11574651 near CD14could influence the efficiency of immune signaling and the intensity of inflammatory reactions, which in turn can significantly impact metabolic pathways, including those involving methionine, as the body redirects resources to support immune function.[3] Such genetic variations are frequently investigated in genome-wide association studies to understand their broad metabolic and immunological implications.[8] Cellular structure, signaling, and protein quality control are also influenced by genetic variations. The ADGRV1 (Adhesion G Protein-Coupled Receptor V1) gene, also known as GPR98, encodes a large adhesion G protein-coupled receptor crucial for cell adhesion, intercellular communication, and tissue development. Variants like rs6891672 in ADGRV1 may impact these fundamental cellular interactions and signaling cascades. The CCT4gene is a subunit of the chaperonin containing TCP1 (CCT) complex, an essential molecular chaperone vital for the correct folding of numerous proteins, including critical cellular components like actin and tubulin. Proper protein folding is indispensable for the activity of enzymes involved in all metabolic pathways, including those that process methionine and its derivatives.[9] A variant such as rs62149891 in CCT4 could potentially affect the efficiency of this complex, leading to protein misfolding and subsequent metabolic dysregulation. Additionally, GPR137B encodes an orphan G protein-coupled receptor involved in various cell signaling processes; genetic variations like rs61833525 could modulate these signal transduction pathways, indirectly impacting cellular responses to metabolic cues and the utilization of essential amino acids such as methionine.[7]Furthermore, gene expression regulation and neurodevelopmental pathways are significantly shaped by genetic variants.ASCL1 (Achaete-Scute Family BHLH Transcription Factor 1) is a basic helix-loop-helix transcription factor that is critical for neurogenesis and neuronal cell fate determination. Genetic variations like rs17450273 in ASCL1could alter its regulatory function, potentially impacting neuronal development and function, processes that are highly dependent on methionine for methylation and neurotransmitter synthesis. Similarly,ZNF285 (Zinc Finger Protein 285) encodes a transcription factor involved in regulating gene expression; a variant like rs2722651 in ZNF285 could modify its binding affinity to DNA, leading to altered expression of target genes crucial for metabolic regulation. The LINC02780 gene produces a long intergenic non-coding RNA (lncRNA), known to modulate gene expression, and a variant like rs10915459 in this lncRNA region could subtly influence gene regulatory networks, impacting cellular functions and methionine-dependent pathways. TheNKAIN3 (Na+/K+ ATPase Interacting 3) gene is involved in modulating the activity of the Na+/K+ ATPase, an enzyme essential for maintaining ion gradients across cell membranes, which is fundamental for nerve impulse transmission and nutrient transport.[10] Variations such as rs145273360 in NKAIN3could affect cellular ion homeostasis, thereby indirectly impacting overall cellular metabolism and the efficient transport of amino acids, including methionine, into cells.[5]Finally, amino acid homeostasis is a complex system where the balance of one amino acid can affect others. TheASPG(Asparaginase) gene encodes an enzyme that catalyzes the hydrolysis of asparagine to aspartate and ammonia. Although not directly involved in methionine metabolism, asparagine metabolism is interconnected with the broader amino acid pool, influencing the availability of precursors for various metabolic pathways. Genetic variations, such asrs1744297 in ASPG, could affect the efficiency of asparagine catabolism, potentially leading to shifts in the overall amino acid balance. These shifts can indirectly impact the cellular demand for other amino acids, including methionine, which is essential for protein synthesis, methylation reactions, and the synthesis of sulfur-containing compounds . Understanding such variants contributes to a comprehensive view of how genetic factors influence amino acid homeostasis and overall metabolic health.[11]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10912810 | FMO4 - SRP14P4 | methionine measurement |
| rs6891672 | ADGRV1 | methionine measurement |
| rs1744297 | ASPG | asparagine measurement, amino acid measurement strand of hair color serine measurement, amino acid measurement amino acid measurement, threonine measurement tryptophan measurement, amino acid measurement |
| rs11574651 | RPL36P11 - CD14 | methionine measurement |
| rs17450273 | ASCL1 - C12orf42-AS1 | tyrosine measurement, amino acid measurement methionine measurement phenylalanine measurement, amino acid measurement |
| rs62149891 | CCT4 | methionine measurement amino acid measurement, threonine measurement |
| rs145273360 | NKAIN3 | methionine measurement |
| rs61833525 | ACO2P2 - GPR137B | methionine measurement |
| rs2722651 | ZNF285 | methionine measurement |
| rs10915459 | LINC02780 - EEF1DP6 | methionine measurement |
Methionine’s Role in Metabolic Profiling
Section titled “Methionine’s Role in Metabolic Profiling”Methionine is a crucial amino acid, representing a fundamental endogenous metabolite whose precise concentration provides insights into the physiological state of the human body.[1] The rapidly advancing field of metabolomics focuses on the comprehensive measurement of such small molecule metabolites in biological fluids, yielding a functional readout of an individual’s metabolic health.[1]Within this framework, methionine, as part of the 18 amino acids typically identified in metabolomics datasets, is routinely profiled in human serum to assess overall metabolic function.[1] This detailed metabolic characterization contributes to a broader understanding of how various biochemical compounds interact and maintain cellular and systemic balance.
Genetic Influence on Amino Acid Homeostasis
Section titled “Genetic Influence on Amino Acid Homeostasis”The steady-state levels, or homeostasis, of key metabolites like amino acids, including methionine, are significantly influenced by an individual’s genetic makeup.[1]Genome-wide association studies (GWAS) aim to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that correlate with variations in these metabolic phenotypes, often referred to as metabotypes.[1] These genetically determined metabotypes are considered intermediate phenotypes that reflect an individual’s metabolic capacity, thereby playing a role in influencing susceptibility to various health outcomes.[1]Understanding these genetic underpinnings helps to explain individual differences in amino acid metabolism and how they might interact with environmental factors.
Contribution to Disease Understanding and Personalized Health
Section titled “Contribution to Disease Understanding and Personalized Health”By integrating genetic data with comprehensive metabolomic profiles, researchers can move beyond simply associating genotypes with clinical outcomes to gain a deeper functional understanding of disease mechanisms.[1]The analysis of genetically determined metabotypes for amino acids, such as methionine, offers more detailed insights into potentially affected pathways in complex diseases.[1] This approach is instrumental in elucidating the pathogenesis of common diseases and exploring gene-environment interactions, ultimately paving the way for personalized health care and nutrition strategies based on an individual’s unique genetic and metabolic characteristics.[1]
Metabolic Pathways and Flux Control
Section titled “Metabolic Pathways and Flux Control”Methionine, as one of the 18 amino acids detected in metabolomics datasets, plays a critical role in various metabolic processes within the human body.[1]While the specific pathways for methionine are not detailed, the broader context of metabolomics studies highlights the importance of comprehensively measuring endogenous metabolites to understand the physiological state.[1] Genetic variants can alter the homeostasis of key amino acids, influencing an individual’s metabotype and susceptibility to complex diseases.[1]The measurement of these intermediate phenotypes provides insights into potentially affected pathways and the overall metabolic network, suggesting that methionine’s synthesis, catabolism, and interconversion would be subject to intricate metabolic regulation and flux control.[1]
Regulatory Mechanisms and Gene-Environment Interactions
Section titled “Regulatory Mechanisms and Gene-Environment Interactions”The homeostasis of metabolites, including amino acids like methionine, is influenced by genetically determined metabotypes, which can interact with environmental factors such as nutrition and lifestyle.[1]Such interactions can impact an individual’s susceptibility to certain phenotypes, implying that regulatory mechanisms like gene regulation and potentially post-translational modifications would govern methionine levels and its metabolic fate.[1] Genome-wide association studies combined with metabolomics aim to identify genetic variants that alter these metabolic profiles, providing a functional understanding of the genetics of complex diseases and the intricate regulatory networks involved in maintaining metabolic balance.[1]
Systems-Level Integration and Network Interactions
Section titled “Systems-Level Integration and Network Interactions”Metabolomics research, by providing access to larger metabolite panels and population sizes, allows for a more detailed probing of the human metabolic network and its associated genetic variants.[1] This approach reveals how changes in specific metabolites, such as amino acids, are integrated across various pathways, indicating significant pathway crosstalk and network interactions.[1]Understanding these systems-level interactions is crucial for elucidating the emergent properties of the metabolic system and how they contribute to overall physiological function, where methionine’s involvement in one pathway could hierarchically affect others.[1]
Disease-Relevant Mechanisms and Therapeutic Targets
Section titled “Disease-Relevant Mechanisms and Therapeutic Targets”Genetically determined metabotypes, which encompass the metabolic profile including amino acids, are recognized as discriminating cofactors in the etiology of common multifactorial diseases.[1]Dysregulation in the pathways involving essential metabolites like methionine would likely contribute to disease pathogenesis. The identification of genetic variants that influence these metabotypes opens avenues for functional investigations into gene-environment interactions and could lead to the identification of therapeutic targets for personalized health care and nutrition.[1]
Genetic Regulation of Amino Acid Homeostasis
Section titled “Genetic Regulation of Amino Acid Homeostasis”The rapidly advancing field of metabolomics aims to comprehensively measure endogenous metabolites, including amino acids, within biological fluids such as human serum. Studies in this area have begun to identify genetic variants that associate with changes in the homeostasis of these key amino acids. Understanding these genetic influences provides a functional readout of the physiological state of the human body and is crucial for deciphering the intricate genetic underpinnings of metabolic networks and their broader implications for human health.[1]
Metabolic Phenotypes and Disease Risk
Section titled “Metabolic Phenotypes and Disease Risk”The identification of genetically determined metabotypes, particularly those involving amino acids, is essential for a functional understanding of complex diseases. Genetic variants that influence the homeostasis of amino acids are anticipated to exhibit associations with various complex traits, thereby contributing to disease etiology. This approach facilitates a more detailed exploration of the human metabolic network and its associated genetic variants, ultimately improving risk assessment and the characterization of overlapping phenotypes.[1]
Personalized Medicine and Monitoring Strategies
Section titled “Personalized Medicine and Monitoring Strategies”Integrating genotyping with metabolomics, especially in the context of amino acid profiles, holds substantial promise for personalized medicine. By identifying specific genetic variants that alter amino acid homeostasis, clinicians may gain the ability to stratify individuals based on their unique metabolic risk profiles. This insight could pave the way for developing individualized medication strategies and more precise monitoring protocols, opening new avenues for investigating gene-environment interactions in the development of complex diseases.[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, vol. 4, no. 11, 2008, e1000282.
[2] 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, 9 Jan. 2009, pp. 60–65.
[3] Benjamin EJ, et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet 8.Suppl 1 (2007): S11. PMID: 17903293.
[4] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, Feb. 2008, pp. 161–69.
[5] Sabatti C, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet 40.11 (2008): 1321–1328. PMID: 19060910.
[6] 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, 26 Sept. 2007, p. 54.
[7] Kathiresan S, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet 40.2 (2008): 189–197. PMID: 19060906.
[8] Wallace C. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet 82.1 (2008): 139–149. PMID: 18179892.
[9] Melzer D, et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet 4.5 (2008): e1000072. PMID: 18464913.
[10] Kooner JS, et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet 40.2 (2008): 198–202. PMID: 18193046.
[11] Doring A, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet 40.4 (2008): 432–436. PMID: 18327256.