Ng Monomethyl Arginine
Introduction
Section titled “Introduction”N-monomethylarginine (NMMA) is a post-translational modification involving the methylation of arginine residues within proteins. This modification is one of several forms of arginine methylation, which also includes asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA). These modifications are precisely regulated by a family of enzymes called protein arginine methyltransferases (PRMTs) and play integral roles in modulating various cellular processes.
Biological Basis
Section titled “Biological Basis”Arginine methylation can significantly influence protein function by altering protein-protein interactions, affecting subcellular localization, and modulating binding to nucleic acids. NMMA, in particular, has been implicated in critical biological processes such as signal transduction, gene expression regulation, and RNA processing. The dynamic nature of these modifications, involving both their formation and removal by specific enzymes, allows for the fine-tuning of protein activity and cellular responses to various stimuli.
Clinical Relevance
Section titled “Clinical Relevance”Dysregulation of arginine methylation, including altered levels or activity of enzymes involved in NMMA generation or metabolism, has been associated with a spectrum of human diseases. Research suggests connections to conditions such as cardiovascular diseases, neurodegenerative disorders, and various forms of cancer. A deeper understanding of NMMA’s specific roles in these pathological states could pave the way for the development of new diagnostic biomarkers or targeted therapeutic interventions. For instance, changes in NMMA levels in biological fluids may serve as indicators of particular disease states or physiological dysfunctions.
Social Importance
Section titled “Social Importance”The ongoing study of arginine methylation, including NMMA, contributes significantly to a more profound understanding of fundamental biological mechanisms that underpin both health and disease. From a broader societal perspective, advancements in this field could lead to the development of novel pharmaceutical agents designed to targetPRMTsor other related enzymes, offering new treatment strategies for conditions that currently lack effective therapies. Moreover, the potential for NMMA to function as a biomarker holds promise for improving early disease detection and facilitating personalized medicine approaches, ultimately enhancing patient care and public health outcomes.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genome-wide association studies (GWAS) face limitations in statistical power, particularly when attempting to detect genetic variants with modest or rare effects for ‘ng monomethyl arginine’. The extensive multiple testing inherent in GWAS requires stringent significance thresholds, which can lead to false negatives or missed associations, especially for variants explaining a small proportion of phenotypic variation. Furthermore, the reliance on imputed genotypes, derived from reference panels like HapMap, introduces a degree of uncertainty, with reported imputation error rates that can impact the accuracy of associations and the comprehensive coverage of genetic variation.[1], [2], [3], [4]A significant challenge lies in distinguishing true genetic associations from potential false positives and prioritizing findings for further investigation. Initial findings often require replication in independent cohorts to confirm their validity, yet non-replication can occur due to differences in study design, statistical power, or the specific genetic variants analyzed across studies. For instance, a reported effect size in an initial study might be larger than in replication, suggesting potential inflation or specific population effects that complicate the interpretation of overall significance.[1], [5], [6]### Generalizability and Phenotype Assessment The majority of current GWAS cohorts primarily consist of individuals of European ancestry, which limits the generalizability of findings for ‘ng monomethyl arginine’ to other diverse ethnic groups. Genetic variants can exhibit substantial frequency differences and varying effect sizes across populations, especially in founder populations where genetic drift may lead to unique, population-specific associations that are difficult to replicate elsewhere. This underscores the need for more diverse cohorts to fully capture the global genetic architecture of ‘ng monomethyl arginine’ and ensure broader applicability of research findings.[1], [7], [8]The precise definition and measurement of ‘ng monomethyl arginine’ can also introduce variability and impact study outcomes. Studies often rely on averaged measurements from multiple observations or may analyze complex derived traits, such as metabolite ratios, to enhance statistical power. While these approaches can be beneficial for reducing noise, they can also influence the estimated effect sizes and the proportion of variance explained, making direct comparisons between studies that use different phenotypic definitions or measurement protocols challenging.[2], [9], [10]### Environmental Interactions and Remaining Knowledge Gaps Genetic influences on ‘ng monomethyl arginine’ are likely modulated by environmental factors, but many studies do not comprehensively investigate these gene-environment interactions. Genetic variants can influence phenotypes in a context-specific manner, meaning their effects might vary depending on lifestyle, diet, or other environmental exposures. Without accounting for these complex interactions, the full biological relevance and clinical applicability of identified genetic associations for ‘ng monomethyl arginine’ may be underestimated, potentially leading to an incomplete understanding of its etiology.[2]
Despite identifying numerous genetic associations, current GWAS designs often do not fully explain the heritability of complex traits like ‘ng monomethyl arginine’. This “missing heritability” may be attributed to several factors, including the inability of current genotyping arrays to capture all causal variants, particularly rare variants or those with complex haplotypic effects, and the lack of functional follow-up to elucidate the precise biological mechanisms by which associated genetic variants influence the trait. Further research, including whole-genome sequencing and functional studies, is crucial to bridge these knowledge gaps and move beyond statistical associations to biological understanding.[1], [5], [6], [11]## Variants
Genetic variants play a significant role in modulating various biological pathways, including those involved in amino acid metabolism and cellular signaling, which can impact the levels of ng monomethyl arginine. Ng monomethyl arginine (NMMA) is a methylated arginine derivative that, along with asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA), is involved in nitric oxide synthesis regulation. Understanding the genetic factors influencing these pathways provides insight into their physiological and pathological implications.
DDAH1(Dimethylarginine Dimethylaminohydrolase 1) is an enzyme crucial for breaking down methylated arginines, such as asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) Variants likers18582 and rs1146382 in DDAH1 may alter this enzyme’s function, influencing the overall balance of methylated arginines. Another gene, SLC7A9(Solute Carrier Family 7 Member 9), is involved in the kidney’s reabsorption of amino acids, including arginine, which is the building block for methylated arginines.[5] A variant such as rs8101881 , located near SLC7A9 and CEP89(Centrosomal Protein 89), could affect arginine availability and thus impact methylation processes. Additionally,MTHFS (5,10-Methenyltetrahydrofolate Synthetase), with its variant rs586023 , is critical for folate metabolism, which provides the methyl groups necessary for protein arginine methyltransferases to produce ng monomethyl arginine (NMMA) and other methylated arginines.[8]These genetic variations collectively influence the complex pathways of arginine methylation and its related biomarkers.
Cellular signaling pathways and inflammatory responses significantly influence metabolic homeostasis, including arginine methylation.MAP2K6 (Mitogen-Activated Protein Kinase Kinase 6) is a key component of the p38 MAPK pathway, a major cellular stress response system that regulates inflammation and gene expression. [11] A variant like rs2188699 in MAP2K6could modulate this pathway’s activity, indirectly affecting enzymes involved in arginine metabolism and thus ng monomethyl arginine (NMMA) levels. Similarly,SPRED2 (Sprouty Related EVH1 Domain Containing 2) inhibits the Ras/MAPK pathway, playing a role in cell growth, differentiation, and inflammatory regulation. [5] Its variant, rs10186240 , may alter this inhibitory function, leading to altered cellular signaling that impacts arginine methylation.BCL10-AS1, an antisense RNA, is linked to BCL10, a protein central to NF-κB signaling and immune responses. [8] The rs18582 variant associated with BCL10-AS1 may influence immune activation, which can, in turn, affect the enzymes that produce or degrade methylated arginines. Furthermore, CSMD1 (CUB And Sushi Multiple Domains 1), with its variant rs1700072 , encodes a complement regulatory protein involved in immune function and neurological processes, where dysregulation could contribute to inflammatory states that indirectly modulate arginine methylation pathways.
Beyond direct metabolic and signaling roles, genes involved in broader cellular functions can also exert indirect influences on arginine methylation.TDRD12(Tudor Domain Containing 12), with its variant While not directly related to arginine metabolism, disruptions in fundamental cellular processes can have cascading effects on overall cellular health and metabolic pathways, potentially impacting the availability of methyl donors or the activity of methyltransferases. Similarly,ANKFN1 (Ankyrin Repeat And FN3 Domain Containing 1), associated with variant rs12103566 , contains protein-protein interaction motifs, suggesting its involvement in various cellular complexes and regulatory networks. [5]Although its precise role in arginine methylation is not yet fully elucidated, genetic variations affecting such broad cellular components can subtly alter the cellular environment, thereby indirectly influencing complex metabolic processes, including the production of ng monomethyl arginine.
Biological Background
Section titled “Biological Background”The provided research studies do not contain specific information regarding ‘ng monomethyl arginine’ to construct a biological background section.
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs18582 | DDAH1, BCL10-AS1 | NG-monomethyl-arginine measurement |
| rs1146382 | DDAH1 | NG-monomethyl-arginine measurement protein measurement |
| rs8101881 | SLC7A9 - CEP89 | metabolite measurement urinary metabolite measurement NG-monomethyl-arginine measurement urate measurement serum creatinine amount |
| rs2188699 | MAP2K6 | NG-monomethyl-arginine measurement |
| rs586023 | MTHFS, ST20-MTHFS | asymmetric dimethylarginine measurement NG-monomethyl-arginine measurement |
| rs4254433 | TDRD12 | NG-monomethyl-arginine measurement |
| rs12103566 | ANKFN1 | NG-monomethyl-arginine measurement |
| rs1700072 | CSMD1 | NG-monomethyl-arginine measurement |
| rs10186240 | SPRED2 | NG-monomethyl-arginine measurement |
References
Section titled “References”[1] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 41, no. 1, 2009, pp. 35–46.
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[3] 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.
[4] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” The American Journal of Human Genetics, vol. 83, no. 5, 2008, pp. 520–528.
[5] 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, S10.
[6] Pare, G., et al. “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 Genetics, vol. 4, no. 12, 2008, e1000308.
[7] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nature Genetics, vol. 40, no. 2, 2008, pp. 189–197.
[8] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, e1000072.
[9] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”The American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60–65.
[10] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genetics, vol. 4, no. 11, 2008, e1000282.
[11] 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, S11.