Trimethyllysine
Trimethyllysine is a post-translationally modified amino acid, formed when three methyl groups are added to the epsilon-amino group of a lysine residue within a protein. This modification is catalyzed by specific methyltransferase enzymes. Trimethyllysine plays a crucial role as a metabolic intermediate and in epigenetic regulation, influencing various cellular processes.
Biological Basis
Section titled “Biological Basis”The primary biological significance of trimethyllysine stems from two main pathways. Firstly, it is a key precursor in the biosynthesis of carnitine, a molecule essential for the transport of long-chain fatty acids into the mitochondria for beta-oxidation, thereby playing a vital role in energy metabolism. Secondly, trimethyllysine is a common modification found on histone proteins, particularly on specific lysine residues within the N-terminal tails of histones H3 and H4. These histone trimethylations (e.g., H3K4me3, H3K9me3, H3K27me3) are critical epigenetic marks that regulate chromatin structure and gene expression. The presence or absence of these marks can activate or repress gene transcription, influencing cellular differentiation, development, and response to environmental cues. Genetic variations, such as single nucleotide polymorphisms (SNPs), can affect the enzymes involved in trimethyllysine modification or metabolism, potentially altering these fundamental biological processes.[1]
Clinical Relevance
Section titled “Clinical Relevance”Dysregulation of trimethyllysine-related pathways has been implicated in a range of clinical conditions. Given its role in carnitine biosynthesis, disruptions can lead to metabolic disorders affecting fatty acid oxidation. Furthermore, aberrant histone trimethylation patterns are strongly associated with various diseases, most notably cancer, where altered epigenetic landscapes can drive tumorigenesis and progression. Neurological disorders, cardiovascular diseases, and developmental abnormalities have also been linked to imbalances in histone methylation. Genetic predisposition to such conditions can be influenced by SNPs affecting the genes encoding methyltransferases, demethylases, or other proteins involved in trimethyllysine metabolism or recognition. Genome-wide association studies (GWAS) frequently identify genetic variants that correlate with various complex traits and disease risks, including those related to metabolic and epigenetic pathways.[1]
Social Importance
Section titled “Social Importance”The study of trimethyllysine and its associated pathways holds significant social importance due to its broad impact on human health and disease. Understanding how genetic variations, including SNPs, influence trimethyllysine levels or the activity of enzymes that modify it, can provide insights into individual susceptibility to metabolic and epigenetic disorders. This knowledge can contribute to the development of personalized medicine approaches, allowing for more targeted diagnostics, prognostics, and therapeutic interventions. Research in this area also furthers our fundamental understanding of gene regulation and metabolism, paving the way for novel strategies to prevent and treat a wide array of human diseases.[1]
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genetic association studies rely on moderate cohort sizes, which can lead to insufficient statistical power and a susceptibility to false negative findings, particularly for associations with modest effects. Conversely, the extensive number of statistical tests performed in genome-wide association studies (GWAS) increases the risk of false positive findings, necessitating rigorous replication in independent cohorts for validation; a previous meta-analysis observed that only about one-third of reported associations were replicated.[2]The ultimate validation of genetic associations, like those for trimethyllysine, therefore requires successful replication and further functional studies.[2]The imputation of untyped single nucleotide polymorphisms (SNPs) is a common practice to enhance coverage across different genotyping platforms, but it introduces a degree of uncertainty with estimated error rates ranging from approximately 1.5% to 2.1% per allele.[3] Furthermore, while efforts were made to standardize analyses across cohorts, such as adjusting for age, gender, and other covariates, some cohort-specific exceptions existed, including variations in covariate inclusion (e.g., age squared not always considered) or the handling of outliers.[4] The assumption of an additive model of inheritance may also oversimplify complex genetic architectures, and the exclusive use of sex-pooled analyses could lead to undetected sex-specific genetic associations.[5]
Generalizability and Population Specificity
Section titled “Generalizability and Population Specificity”A significant limitation across many of these genetic studies is the predominant reliance on cohorts of white European ancestry, which restricts the generalizability of findings to other ethnic and racial groups.[2] While some studies attempted to extend findings to multiethnic samples, the initial discovery and replication phases were largely confined to individuals of European descent, necessitating further research in diverse populations to confirm the universality of observed associations.[4] Cohorts often comprise middle-aged to elderly individuals, potentially introducing a survival bias and limiting the applicability of results to younger populations.[2] Despite efforts to mitigate population stratification using methods like genomic control and principal component analysis, residual stratification can still influence association results, particularly in studies that are not entirely immune to such effects.[6] Therefore, care must be taken when extrapolating findings to populations with different genetic backgrounds or demographic structures.
Phenotype Definition and Confounding Factors
Section titled “Phenotype Definition and Confounding Factors”Phenotype definitions and measurement protocols varied across studies, with some employing log-transformed values and multivariable-adjusted residuals as phenotypes, which, while standard, can complicate direct comparisons of trimethyllysine levels.[4] The exclusion of individuals on lipid-lowering therapy from some analyses, while necessary to avoid treatment effects, means the findings may not fully represent the broader population, particularly those requiring such interventions.[4]The influence of environmental factors, gene-environment interactions, and other unmeasured confounders remains a substantial knowledge gap; for instance, while some studies adjusted for variables like age and diabetes status, comprehensive data on lifestyle, diet, and other environmental exposures that could modulate genetic effects were not consistently available.[4]Furthermore, the use of certain biomarkers, such as TSH for thyroid function or cystatin C for kidney function, without comprehensive related measures (e.g., free thyroxine or GFR-transforming equations), means that the measured phenotypes might not fully capture the underlying biological processes or may reflect additional confounding cardiovascular risks.[7]These limitations highlight the need for more comprehensive phenotyping and environmental data collection in future research on trimethyllysine.
Variants
Section titled “Variants”Genetic variations, particularly single nucleotide polymorphisms (SNPs), play a fundamental role in shaping individual traits and disease susceptibility by influencing gene function and regulatory pathways. Thers562044044 variant is associated with the DPYSL2gene, which encodes Dihydropyrimidinase-related protein 2, a crucial component in neuronal development, axon guidance, and cell migration within the brain. Alterations introduced by this variant could impact the protein’s structure or expression, thereby affecting these complex neurological processes. Similarly,rs78078192 is linked to TBC1D7 (TBC1 domain family member 7), a gene involved in essential cellular activities like membrane trafficking and autophagy, acting as a regulator for small GTPases that control vesicle movement and cellular waste recycling. This variant might influence TBC1D7 protein function, potentially disrupting these pathways vital for cellular homeostasis and the prevention of damaged component accumulation. Studies in human genetics consistently show that DNA variants influence human diseases and protein levels.[8]Furthermore, post-translational modifications, such as trimethyllysine, are critical for regulating protein activity and stability, and dysregulation caused by variants in genes likeDPYSL2 or TBC1D7 could indirectly affect these modification patterns, impacting protein interactions or cellular signaling.[6] Non-coding RNA elements, including long intergenic non-coding RNAs (lncRNAs) and pseudogenes, are increasingly recognized for their diverse regulatory roles in the genome. The rs75233056 variant, associated with the LINC01967-CMC1 region, could influence the expression or stability of LINC01967, a lncRNA, while CMC1(Cysteine-rich transmembrane module containing 1) is a protein-coding gene. Similarly,rs34568450 is located in the RN7SL148P-SPMIP3 region, involving pseudogenes which can act as molecular sponges for microRNAs or influence chromatin structure. Such genetic variations can significantly influence gene expression levels, impacting various cellular functions.[8]These non-coding RNAs and pseudogenes often play a role in recruiting epigenetic modifiers, including those that catalyze or recognize trimethyllysine marks on histones, thereby modulating gene transcription and overall chromatin accessibility. Therefore, variants affecting these non-coding elements could indirectly lead to widespread alterations in the epigenetic landscape, influencing cellular identity and function.[2] The rs150604736 variant is associated with the IL20RA and IL22RA2genes, which encode components of cytokine receptors crucial for immune and inflammatory responses.IL20RA is part of receptors for IL-19, IL-20, and IL-24, important in skin and epithelial immunity, while IL22RA2 acts as a soluble decoy receptor for IL-22, modulating its pro-inflammatory or protective effects. Changes introduced by rs150604736 could alter the balance of cytokine signaling, affecting the body’s response to infection, inflammation, or autoimmune conditions. Concurrently, thers11133993 variant is linked to LINC01019, another lncRNA, and IRX1(Iroquois homeobox 1), a transcription factor vital for developmental processes and cell differentiation. Research indicates that DNA variations can impact a wide array of physiological traits and disease risks . Both immune regulation and developmental pathways are tightly controlled by epigenetic mechanisms, including the precise placement and removal of trimethyllysine marks on histones and non-histone proteins. Variants influencing receptor function or transcription factor activity, such as those inIL20RA-IL22RA2 or IRX1, can consequently alter the gene expression programs governed by these epigenetic modifications, leading to downstream effects on cellular function and organismal development.[9]Based on the researchs materials, there is no information available to construct a biological background section for ‘trimethyllysine’.
Key Variants
Section titled “Key Variants”References
Section titled “References”[1] McCarthy, M. I., et al. “Genome-Wide Association Studies for Complex Traits: Consensus, Uncertainty and Challenges.” Nat Rev Genet, vol. 9, no. 5, 2008, pp. 356–369.
[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, S9.
[3] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-9.
[4] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, vol. 40, no. 2, 2008, pp. 189-97.
[5] 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, suppl. 1, 2007, S11.
[6] Uda, M., et al. “Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia.”Proc Natl Acad Sci U S A, vol. 105, no. 5, 2008, pp. 1620-5.
[7] Hwang, S. J., et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, S10.
[8] Melzer, D., et al. “A Genome-Wide Association Study Identifies Protein Quantitative Trait Loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.
[9] Wallace, C., et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 132-41.