N Acetylmethionine
Background
Section titled “Background”N-acetylmethionine is an acetylated derivative of methionine, an essential sulfur-containing amino acid crucial for numerous biological processes. Methionine plays a vital role in protein synthesis, acts as a precursor for other sulfur compounds like cysteine and taurine, and is a key donor of methyl groups through its activated form, S-adenosylmethionine, which is involved in countless methylation reactions across the body. The acetylation of amino acids, such as methionine, can alter their chemical properties, affecting their stability, transport across cell membranes, or their specific metabolic pathways.
Biological Basis
Section titled “Biological Basis”The biological role of N-acetylmethionine is believed to be intertwined with methionine metabolism. It may function as a protected or alternative form of methionine, potentially influencing its availability for essential processes or serving as a detoxification product. As a derivative, it could be involved in regulating the cellular pool of methionine or participate in sulfur and one-carbon metabolic cycles. The levels of N-acetylmethionine in biological fluids can reflect an individual’s metabolic state, dietary intake, and the activity of enzymes involved in its synthesis or degradation.
Clinical Relevance
Section titled “Clinical Relevance”Understanding the metabolism and physiological impact of compounds like N-acetylmethionine is a central goal of metabolomics, a field dedicated to comprehensively measuring endogenous metabolites in biological samples.[1]Variations in amino acid metabolism, including that of methionine and its derivatives, are frequently linked to various health conditions. Therefore, N-acetylmethionine could potentially serve as a biomarker for specific metabolic disorders or physiological states. Genetic factors, such as single nucleotide polymorphisms (SNPs), are known to influence the homeostasis of key metabolites, including amino acids, and these associations can provide insights into disease mechanisms.[1]Studies employing genome-wide association (GWA) approaches to analyze metabolite profiles have identified genetic variants associated with diverse biochemical parameters, highlighting the genetic influence on circulating metabolite levels.[1]
Social Importance
Section titled “Social Importance”The study of N-acetylmethionine, within the broader context of metabolomics and genomics, holds significant social importance. By elucidating its metabolic pathways and potential genetic influences, researchers can contribute to a deeper understanding of human metabolism and its implications for health. This knowledge can pave the way for advancements in personalized medicine, allowing for more tailored dietary interventions, improved diagnostic tools, and the development of novel therapeutic strategies for conditions involving amino acid dysregulation. The integration of genetic information with metabolomic data is crucial for unraveling the complex interplay between an individual’s genetic makeup and their metabolic phenotype, ultimately impacting public health through precision approaches.[1]
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”Studies investigating genetic associations with ‘n acetylmethionine’ may encounter limitations related to study design and statistical power. Many research efforts are characterized by moderate cohort sizes, which can diminish the statistical power required to robustly detect genetic effects of modest magnitude, particularly when accounting for the extensive multiple testing inherent in genome-wide association studies.[2] This situation can lead to an increased risk of false-positive findings, which underscores the necessity for rigorous replication in independent cohorts and validation through functional studies. [2] Indeed, a substantial proportion of reported genetic associations do not consistently replicate across different study populations. [2]
Replication of ‘n acetylmethionine’ associations can be further complicated by variations in study methodologies, statistical power, and the specific genetic markers analyzed across different investigations.[3] Genotyping platforms, especially older versions with limited SNP coverage, may not comprehensively capture all genetic variation, potentially leading to false negative results or an inability to replicate previously identified associations. [4] While imputation methods can expand genomic coverage, they introduce an estimated error rate that can affect the precision of reported associations. [5] Additionally, analyses focusing solely on sex-pooled data, rather than sex-specific analyses, might miss associations that are present only in one gender. [6]
Generalizability and Phenotypic Assessment
Section titled “Generalizability and Phenotypic Assessment”A significant limitation for research on ‘n acetylmethionine’ is the often-restricted demographic makeup of the study populations, which frequently consist predominantly of individuals of white European ancestry.[7] This lack of diversity in terms of ethnicity, age, and geographical origin limits the direct applicability of findings to younger populations or individuals from different racial or ethnic backgrounds, where genetic predispositions and environmental exposures may vary considerably. [2] Consequently, genetic associations identified in these homogeneous cohorts may not be universally generalizable, highlighting the need for broader representation in future studies.
The accuracy and consistency of phenotypic measurements, particularly for traits like ‘n acetylmethionine’ or related biomarkers, are crucial for robust genetic association analyses. Methodological choices, such as using proxy measures or simplified indicators when direct, precise assessments are unavailable, can introduce variability and potentially obscure true biological relationships.[8] For instance, relying on single or averaged measurements over time, or employing specific assay methods that may not be universally validated, can impact the power to detect associations or lead to missed bivariate relationships. [4] Furthermore, biases inherent to cohort recruitment, such as survival bias from DNA collection at later study examinations, could inadvertently influence the observed genetic associations. [2]
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”Many studies investigating ‘n acetylmethionine’ do not extensively explore gene-environment interactions, which are critical for understanding how genetic predispositions are modulated by environmental factors. Genetic effects can be context-dependent, meaning that environmental influences, such as diet or lifestyle, may alter the expression or magnitude of genetic associations.[4]The absence of comprehensive analyses into these interactions may lead to an incomplete understanding of the complex etiology of ‘n acetylmethionine’ levels or related phenotypes, potentially overlooking important environmental modifiers.
Despite advancements in genome-wide association studies, a substantial portion of the heritability for complex traits, including those potentially related to ‘n acetylmethionine’, often remains unexplained. Current genotyping arrays and imputation strategies, while powerful, may not capture all genetic variation, particularly rare variants, structural variants, or genomic regions that are not well-represented in reference panels.[6]This “missing heritability” suggests that a complete genetic understanding of ‘n acetylmethionine’ will require larger sample sizes, more comprehensive sequencing technologies, and innovative statistical approaches to identify the full spectrum of causal variants.[9]
Variants
Section titled “Variants”Variants across several genes play roles in diverse metabolic processes, cellular architecture, and inflammatory responses, with potential implications for the metabolism of N-acetylmethionine and related pathways. The enzymeACY1(Acylase 1) is a cytosolic enzyme crucial for detoxifying and metabolizing N-acylated amino acids, including N-acetylmethionine, by hydrolyzing them into their constituent L-amino acids and acetate. The variant*rs121912698 *, associated with ACY1 and the adjacent ABHD14A, may influence the efficiency of this enzymatic activity, thereby impacting the availability of free methionine for vital cellular functions like methylation and protein synthesis.[10] Similarly, *rs150416778 *, located near ABHD14A-ACY1 and ABHD14B, could affect gene expression or protein function, potentially altering the broader metabolic landscape and the processing of N-acetylated compounds. Furthermore, the *rs3747207 * variant in PNPLA3(Patatin-like phospholipase domain-containing protein 3) is a well-established genetic factor in lipid metabolism, particularly influencing triglyceride hydrolysis and contributing to conditions like non-alcoholic fatty liver disease. Changes in lipid metabolism due to this variant might indirectly affect methionine metabolism, as methionine is a precursor for phospholipids and a key methyl donor involved in hepatic lipid handling.[1]
Other variants impact fundamental cellular structure and signaling pathways, which are intrinsically linked to metabolic health. DOCK3(Dedicator of cytokinesis 3) is a guanine nucleotide exchange factor that helps regulate cytoskeletal dynamics and neuronal development. The*rs544108035 * variant in DOCK3could affect cellular signaling and morphology, potentially modulating cellular responses to metabolic cues or nutrient availability, including those involving methionine.POC1A (Protein of centriole 1A) is vital for centriole biogenesis and ciliary function, processes critical for cell division and various signaling pathways. Variants such as *rs190202562 * and *rs148804382 *, which are found within or near POC1A (and the adjacent pseudogene ALDOAP1), may alter cellular organization and signaling, thereby influencing metabolic homeostasis and potentially affecting the demand for or utilization of N-acetylmethionine.[2] The FNDC3A (Fibronectin type III domain-containing protein 3A) gene, with its *rs186662316 *variant, is involved in cell adhesion and extracellular matrix interactions, processes that communicate with intracellular metabolism. Alterations in these interactions could influence cell growth and repair mechanisms, which are highly dependent on adequate amino acid supply and metabolic support .
Genetic variations also contribute to protease activity and disease susceptibility, with broad metabolic implications.ITIH3 (Inter-alpha-trypsin inhibitor heavy chain H3) is part of a family of proteins that stabilize the extracellular matrix and inhibit proteases, particularly in inflammatory contexts. The *rs545740325 * variant in ITIH3could influence extracellular matrix integrity or inflammatory responses, both of which can significantly alter metabolic demands and the turnover of proteins, thereby impacting the pool of available amino acids like methionine.ALMS1(Alström syndrome protein 1) is associated with Alström syndrome, a multi-system genetic disorder characterized by metabolic disturbances including obesity and type 2 diabetes. The*rs6710438 * variant in ALMS1may contribute to metabolic dysregulation, which often involves altered amino acid metabolism and could affect the processing or requirements for N-acetylmethionine.[3] Lastly, PRSS50(Serine protease 50) encodes a serine protease, a class of enzymes involved in diverse biological processes such as protein degradation, digestion, and immune function. The*rs757041983 * variant in PRSS50could modify protease activity, potentially affecting protein turnover and the availability of amino acids for metabolic pathways, including those involving methionine and its derivatives, thereby influencing overall protein homeostasis and nutrient sensing.[6]
There is no information about ‘n acetylmethionine’ in the provided research materials. Therefore, a biological background section for this compound cannot be generated based on the given context.
There is no information about ‘n acetylmethionine’ in the provided research.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs121912698 | ACY1, ABHD14A-ACY1 | protein measurement vitamin D amount IGF-1 measurement 2-aminooctanoate measurement propionylglycine measurement |
| rs544108035 | DOCK3 | N-acetylmethionine measurement |
| rs545740325 | ITIH3 | N-acetylalanine measurement N-acetylserine measurement N-acetylvaline measurement N-formylmethionine measurement N-acetylmethionine measurement |
| rs6710438 | ALMS1 | serum metabolite level N-acetylmethionine measurement |
| rs190202562 | POC1A - ALDOAP1 | N-acetylserine measurement N-formylmethionine measurement N-acetylmethionine measurement N-acetylalanine measurement |
| rs150416778 | ABHD14A-ACY1, ABHD14B | N-formylmethionine measurement N-acetylmethionine measurement N-acetylserine measurement N-acetylalanine measurement protein measurement |
| rs148804382 | POC1A | N-acetylalanine measurement N-acetylmethionine measurement N-acetylserine measurement |
| rs757041983 | PRSS50 | N-acetylalanine measurement N-formylmethionine measurement N-acetylmethionine measurement |
| rs3747207 | PNPLA3 | platelet count serum alanine aminotransferase amount aspartate aminotransferase measurement triglyceride measurement non-alcoholic fatty liver disease |
| rs186662316 | FNDC3A | N-acetylmethionine measurement |
References
Section titled “References”[1] Gieger, Christian, et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet 5.11 (2009): e1000694.
[2] Benjamin, E. J., et al. “Genome-Wide Association with Select Biomarker Traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S10.
[3] Sabatti, C., et al. “Genome-Wide Association Analysis of Metabolic Traits in a Birth Cohort from a Founder Population.”Nature Genetics, vol. 40, no. 12, 2008, pp. 1394-1402.
[4] Vasan, R. S., et al. “Genome-Wide Association of Echocardiographic Dimensions, Brachial Artery Endothelial Function and Treadmill Exercise Responses in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S2.
[5] 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, pp. 161-169.
[6] Yang, Q., et al. “Genome-Wide Association and Linkage Analyses of Hemostatic Factors and Hematological Phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S9.
[7] Melzer, D. “A Genome-Wide Association Study Identifies Protein Quantitative Trait Loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, p. e1000072.
[8] Hwang, S. J., et al. “A Genome-Wide Association for Kidney Function and Endocrine-Related Traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, p. S11.
[9] Kathiresan, S., et al. “Common Variants at 30 Loci Contribute to Polygenic Dyslipidemia.” Nature Genetics, vol. 40, no. 12, 2008, pp. 1421-1430.
[10] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.