Isoeugenol Sulfate
Background
Section titled “Background”Isoeugenol sulfate is a metabolite derived from isoeugenol, a naturally occurring phenolic compound found in the essential oils of various plants, including ylang-ylang, nutmeg, and basil. Isoeugenol is widely used in fragrances, flavorings, and cosmetics due to its pleasant aroma. When isoeugenol is absorbed into the body, it undergoes metabolic transformation, one common pathway being sulfation, which converts it into isoeugenol sulfate.
Biological Basis
Section titled “Biological Basis”The formation of isoeugenol sulfate is a key process in the body’s detoxification system, specifically a phase II biotransformation reaction. In this process, sulfotransferase enzymes add a sulfate group to the isoeugenol molecule. This chemical modification typically increases the compound’s water solubility, facilitating its excretion from the body through urine or bile. Generally, sulfation is considered a detoxification pathway that renders the parent compound less biologically active or, in some cases, completely inactive. The efficiency of this metabolic pathway can vary among individuals due to genetic differences in the sulfotransferase enzymes involved.
Clinical Relevance
Section titled “Clinical Relevance”While isoeugenol itself can exhibit various biological activities, such as antimicrobial, antioxidant, or anti-inflammatory effects, its sulfated form, isoeugenol sulfate, is largely considered an inactive or less active metabolite. The rate and extent of isoeugenol sulfation can influence the systemic exposure to the parent compound, potentially impacting individual responses. Variations in sulfation capacity, often influenced by genetic polymorphisms in sulfotransferase genes, might affect how individuals process isoeugenol. For individuals with sensitivities or allergies to isoeugenol, differences in their metabolic capacity to form isoeugenol sulfate could play a role in their overall reaction profile.
Social Importance
Section titled “Social Importance”Isoeugenol’s extensive use in consumer products, ranging from perfumes and soaps to food additives, makes its metabolism and excretion processes relevant for public health. Understanding the formation of metabolites like isoeugenol sulfate is crucial for assessing the safety and potential impact of these ubiquitous compounds. Individual differences in the capacity to sulfate isoeugenol contribute to the broader understanding of chemical exposure and personalized responses to common environmental and dietary substances, informing regulatory guidelines and consumer product development.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Current research, primarily employing Genome-Wide Association Study (GWAS) approaches, acknowledges several inherent methodological and statistical limitations. Early GWAS often utilized SNP arrays with limited coverage, such as 100K arrays or subsets of HapMap SNPs, which may miss genuine genetic associations due to incomplete representation of common genetic variation across the genome. [1] This lack of comprehensive coverage can hinder the ability to fully characterize candidate genes or detect novel loci, potentially leading to an underestimation of the genetic contribution to a trait. Furthermore, to manage the multiple testing problem inherent in GWAS, some analyses were performed as sex-pooled, which could mask sex-specific genetic associations that might only manifest in males or females. [1]
The moderate sample sizes of some cohorts have also contributed to insufficient statistical power, increasing the susceptibility to false negative findings. [2] This is further compounded by the challenge of replicating initial findings, where only a fraction of reported associations consistently replicate across different studies, suggesting that some initial discoveries may represent false positives. [2] Additionally, reliance on statistical models that include SNPs based on specific p-value thresholds or that combine data from different stages of discovery can introduce selection bias, potentially inflating effect sizes or failing to capture the full spectrum of genetic influences. [3]
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”A significant limitation across many genetic studies is the restricted generalizability of findings, primarily due to cohort demographics. Many large-scale studies have predominantly recruited individuals of white European or Caucasian descent, often middle-aged to elderly. [2] This narrow demographic range means that results may not be directly transferable to younger populations or individuals of other ethnic or racial backgrounds, where genetic architecture and environmental exposures may differ substantially. The inclusion of DNA samples from later examinations in longitudinal studies can also introduce survival bias, potentially skewing the genetic profiles observed. [2]
Phenotype measurement also presents challenges. For instance, specific assay methods might fail to detect certain genetic variants, leading to their exclusion from analysis and potentially incomplete understanding of a trait’s genetic basis. [3]While stringent quality control measures, such as excluding SNPs with low minor allele frequency, deviation from Hardy-Weinberg equilibrium, or low call rates, are crucial for robust analyses, they can also reduce the final sample size or alter the genetic landscape under investigation, influencing the precision and scope of findings.[4]
Unexplained Heritability and Future Directions
Section titled “Unexplained Heritability and Future Directions”Despite the identification of numerous genetic associations, a substantial portion of the heritability for complex traits often remains unexplained. For example, even for traits with relatively clear genetic influences, identified variants may account for only a fraction of the total genetic variation, highlighting the phenomenon of “missing heritability”. [5] This suggests that many genetic factors, including rare variants, structural variations, or complex gene-gene and gene-environment interactions, are yet to be discovered or fully understood.
The current body of GWAS research indicates a critical need for subsequent functional studies to validate identified associations and elucidate the biological mechanisms through which these genetic variants influence traits. [2] GWAS data, while powerful for discovery, are generally insufficient for comprehensively studying a candidate gene in isolation. [1] The ongoing challenge lies in prioritizing promising SNPs for further investigation and translating genetic associations into a deeper understanding of pathophysiology and potential therapeutic targets.
Variants
Section titled “Variants”NKAIN2(Na+/K+ transporting ATPase interacting protein 2) plays a crucial role in regulating the activity of the Na+/K+-ATPase, an essential enzyme responsible for maintaining electrochemical gradients across cell membranes. These gradients are fundamental for numerous cellular processes, including nutrient transport, waste removal, volume regulation, and particularly, neuronal excitability. A single nucleotide polymorphism, such asrs17627947 , located within or near the NKAIN2 gene, can subtly influence its expression, stability, or its interaction with the Na+/K+-ATPase, thereby affecting overall cellular function and homeostasis. Proper cellular membrane structure, influenced by genetic factors affecting lipids like phosphatidylcholines, is vital for these integrated cellular operations. [6]
The intricate balance maintained by proteins like NKAIN2is essential for cellular homeostasis, which directly impacts how a cell responds to and processes various metabolites, including xenobiotics. Isoeugenol sulfate, as a sulfur-conjugated compound, represents a product often associated with the body’s detoxification pathways, requiring precise metabolic processing. AlthoughNKAIN2 does not directly metabolize such compounds, its influence on cellular energetics, membrane transport mechanisms, and overall cell health can indirectly affect the efficiency of these metabolic processes and the transport of metabolites or their precursors and products. For instance, the ability of cells to handle complex chemical compounds is closely tied to overall cellular metabolic capacity and membrane integrity. [6]
Genetic variations like rs17627947 can contribute to individual differences in NKAIN2activity, leading to varied cellular resilience and metabolic efficiency among people. Such polymorphisms exemplify gene-environment interactions, where an individual’s genetic makeup influences their response to external factors, including dietary components or environmental chemicals. The way an individual processes and responds to compounds like isoeugenol sulfate may thus be subtly modulated by a genetic background that impacts core cellular machinery and metabolic pathways. Understanding these variations helps to explain differences in individual metabolic profiles and sensitivities to various compounds.[4]
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Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs17627947 | NKAIN2 | isoeugenol sulfate measurement |
Biological Background
Section titled “Biological Background”Genetic Regulation of Metabolite Levels
Section titled “Genetic Regulation of Metabolite Levels”The circulating levels of various endogenous metabolites in human serum are significantly influenced by an individual’s genetic makeup. Genome-wide association studies (GWAS) have emerged as a powerful tool to identify specific genetic variants that associate with changes in the homeostasis of key metabolites, including lipids, carbohydrates, and amino acids ([6]). Such genetic variations can impact gene function, the activity of regulatory elements, and overall gene expression patterns, thereby modulating metabolite concentrations. For example, variations in genes like MLXIPLhave been linked to plasma triglyceride levels ([7]), while common single nucleotide polymorphisms (SNPs) inHMGCRare associated with low-density lipoprotein cholesterol (LDL-C) levels and affect the alternative splicing of its exon 13, influencing cholesterol synthesis pathways ([8]). These findings underscore the intricate genetic architecture underlying human metabolic profiles.
Molecular and Cellular Pathways in Metabolite Homeostasis
Section titled “Molecular and Cellular Pathways in Metabolite Homeostasis”Metabolite levels are maintained through complex molecular and cellular pathways that involve synthesis, degradation, transport, and interconversion. These processes are orchestrated by critical enzymes, transporters, and other structural components within cells. For instance, the transport of specific metabolites across cellular membranes is often facilitated by specialized proteins, such as SLC2A9 (Solute Carrier Family 2 Member 9) and GLUT9(Glucose Transporter 9), which are known to influence serum uric acid concentrations and urate excretion ([9]). Beyond transport, metabolic processes like the mevalonate pathway, crucial for cholesterol synthesis, involve a series of enzymatic steps, with HMGCR being a key regulatory enzyme in this cascade ([8]). The comprehensive measurement of these endogenous metabolites offers a functional readout of the physiological state, highlighting the interconnection of various biochemical pathways ([6]).
Systemic Integration and Physiological Readouts
Section titled “Systemic Integration and Physiological Readouts”Metabolite profiles in human serum provide a broad reflection of systemic physiological states and interactions across different tissues and organs. Genetic variants that influence these profiles act as “intermediate phenotypes,” offering detailed insights into potentially affected pathways throughout the body ([6]). For example, while genes like SLC2A9primarily impact uric acid levels, this has systemic implications for conditions like gout ([9]). Similarly, variations in lipid-regulating genes, such as those influencing LDL-C, high-density lipoprotein cholesterol (HDL-C), or triglycerides, have widespread systemic consequences, impacting cardiovascular health ([10]). Thus, changes in individual metabolite levels can perturb homeostatic balance, affecting overall bodily function and contributing to systemic health outcomes.
Pathophysiological Relevance and Disease Mechanisms
Section titled “Pathophysiological Relevance and Disease Mechanisms”Disruptions in metabolite homeostasis are often central to the mechanisms of various diseases and developmental processes. Understanding the genetic and molecular underpinnings of metabolite regulation can elucidate pathophysiological pathways and potential therapeutic targets. Abnormal levels of metabolites, for example, lipids such as triglycerides and cholesterol, are well-established risk factors for complex traits like coronary artery disease and subclinical atherosclerosis ([7], [10]). Furthermore, altered uric acid metabolism, influenced by genes likeSLC2A9 and GLUT9, is directly implicated in the development of gout ([9]). These associations highlight how genetic determinants of metabolite profiles contribute to susceptibility to disease and shape the body’s compensatory responses to metabolic challenges.
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References
Section titled “References”[1] Yang Q. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, 2007.
[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007.
[3] Pare G. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genet, 2008.
[4] Dehghan A. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, 2008.
[5] Benyamin, Beben, et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60-65.
[6] Gieger C. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, 2008.
[7] Kooner, J. S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, 2008.
[8] Burkhardt, R., et al. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol, 2008.
[9] Vitart, V., et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, 2008.
[10] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008.