N Formylphenylalanine
n-Formylphenylalanine is a modified derivative of phenylalanine, an essential aromatic amino acid vital for protein synthesis and neurotransmitter production. As a metabolite, it represents one of many small molecules involved in the complex biochemical network known as the human metabolome, which reflects the body’s current physiological state.
Biological Basis
Section titled “Biological Basis”The formation of n-formylphenylalanine typically involves the addition of a formyl group to phenylalanine. In biological systems, formylation can occur through various metabolic pathways, potentially serving roles in detoxification, as intermediates in specialized biosynthetic processes, or even as signaling molecules. The comprehensive study of such metabolites, known as metabolomics, aims to provide a functional readout of the physiological state by measuring endogenous metabolites in bodily fluids.[1] Understanding the intricate balance (homeostasis) of amino acids and their derivatives is a key goal of this field. [1]
Clinical Relevance
Section titled “Clinical Relevance”Genetic variations can influence the levels and metabolism of diverse biochemical parameters, including amino acid derivatives like n-formylphenylalanine. These variations contribute to individual differences in metabolic traits and can impact overall health.[1] Such biochemical parameters are frequently measured in routine clinical care, and insights into their genetic underpinnings can offer a deeper understanding of various health conditions. [2] For instance, genome-wide association studies (GWAS) have demonstrated links between genetic variants and metabolic traits, highlighting the potential for these metabolites to serve as biomarkers or therapeutic targets. [3]
Social Importance
Section titled “Social Importance”The study of metabolites and their genetic influences holds significant social importance by advancing our understanding of human health and disease. By linking specific genetic variants to changes in metabolite profiles, researchers can identify novel pathways involved in disease development and progression. This knowledge can contribute to the development of personalized medicine approaches, improved disease diagnostics, and more targeted interventions for metabolic disorders and other health challenges.[1]
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic association studies face several methodological and statistical challenges that influence the interpretation and reliability of their findings. Many studies, particularly those with moderate cohort sizes, are inherently susceptible to false-negative findings due to inadequate statistical power. [4] This limitation is compounded by the significant burden of multiple testing in genome-wide association studies (GWAS), which necessitates stringent significance thresholds that may obscure true, yet more subtle, genetic effects. A critical constraint is the challenge of replication; many initial associations reported in GWAS do not consistently replicate across independent cohorts, raising concerns about false positives or variations in study populations. [5]
Furthermore, choices in study design can introduce limitations. For instance, performing only sex-pooled analyses might miss crucial genetic variants that exhibit sex-specific associations with phenotypes. [6] The reliance on imputation to infer genotypes for untyped or disparate marker sets, while broadening genomic coverage, introduces potential error rates that can affect the accuracy of associations. [7] Additionally, within family-based or related cohorts, failing to adequately model polygenic effects and account for individual relatedness can lead to inflated false-positive rates and misleading statistical inferences. [7]
Phenotypic Assessment and Measurement Challenges
Section titled “Phenotypic Assessment and Measurement Challenges”The precise and consistent measurement of phenotypes is paramount, yet often presents considerable challenges in genetic research. For complex traits, averaging measurements over long periods can introduce misclassification, especially when different equipment or methodologies are employed over time. [8] Such averaging may also inadvertently mask age-dependent genetic effects, as the genetic and environmental influences on a trait can evolve across different life stages. [8] Specific biomarkers, such as those related to iron status, can exhibit significant variability influenced by environmental factors like the time of day blood is collected or an individual’s menopausal status, potentially confounding genetic associations if not rigorously controlled. [9]
Moreover, the analytical approach to phenotypes can introduce limitations. Many biological measures deviate from a normal distribution, necessitating various statistical transformations (e.g., log, Box-Cox) to meet the assumptions of association tests. [10] The selection and application of these transformations must be carefully considered, and the robustness of findings often requires verification across different methods. [10] In some instances, studies may rely on indirect or surrogate markers due to a lack of comprehensive direct measures, which can limit the specificity and applicability of the findings to the underlying biological processes. [5]
Generalizability and Unexplained Variation
Section titled “Generalizability and Unexplained Variation”A pervasive limitation across many genetic studies is the restricted diversity of participant cohorts, predominantly comprising individuals of white European descent. [10] This demographic homogeneity inherently limits the generalizability of genetic discoveries to other ethnic or racial populations, where genetic architectures, allele frequencies, and environmental exposures may differ substantially. [4] While sophisticated methods can mitigate population stratification, the fundamental issue of underrepresentation hinders the translation of findings to a global context. [11] Furthermore, issues such as survival bias, which can arise when DNA is collected from participants at later stages of a longitudinal study, might subtly alter the observed genetic associations within a cohort. [4]
Finally, a considerable proportion of the heritability for many complex traits remains unexplained by identified genetic variants, a phenomenon termed “missing heritability”. [3] Even for traits with high estimated heritability, the combined effects of discovered loci often account for a small percentage of trait variability, suggesting that numerous additional genetic factors—including common variants with very small effects, rare variants, and complex gene-environment interactions—are yet to be discovered. [3] The comprehensive assessment of environmental confounders and their intricate interplay with genetic predispositions remains a significant knowledge gap, crucial for fully elucidating the etiology of complex phenotypes. [3]
Variants
Section titled “Variants”The PAHgene, located on chromosome 12, encodes the enzyme phenylalanine hydroxylase, which is vital for metabolizing the amino acid phenylalanine. This enzyme converts phenylalanine into tyrosine, a crucial step in amino acid metabolism.[1] Variants within the PAH gene, such as rs35350743 , can reduce the enzyme’s activity or production, leading to impaired phenylalanine breakdown. The inefficiency of this conversion results in the accumulation of phenylalanine in the body, a hallmark of the genetic disorder phenylketonuria (PKU).[12] The specific impact of rs35350743 on enzyme function can vary, contributing to the diverse clinical presentations of PKU.
Impaired phenylalanine hydroxylase activity, often due to genetic variants likers35350743 , causes phenylalanine to reach elevated levels. When the primary metabolic pathway is blocked, excess phenylalanine is shunted into alternative pathways, leading to the production of various phenylalanine derivatives.[2]One such related compound is n formylphenylalanine; while not a direct product of thePAHenzyme, its levels or metabolism could be indirectly affected by the overarching disruption in phenylalanine homeostasis. Abnormal accumulation of phenylalanine and its byproducts can be neurotoxic, influencing brain development and function, making it important to monitor these metabolites in individuals withPAH variants. [1]
The clinical implications of PAHvariants are significant, as untreated classical PKU leads to severe neurological damage, including intellectual disability, developmental delays, and behavioral issues. Early diagnosis through newborn screening and lifelong dietary management, which restricts phenylalanine intake, are critical for preventing these devastating outcomes.[10] Research continues to explore the full spectrum of PAHvariants and their precise effects on phenylalanine and its downstream metabolites, including compounds like n formylphenylalanine, to refine diagnostic tools and personalize therapeutic strategies for individuals living with PKU.[1]
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Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs35350743 | PAH | N-formylphenylalanine measurement |
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.
[2] 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. 139–149.
[3] Sabatti, Chiara, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2008.
[4] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 57.
[5] Hwang, Shih-Jen, 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, 2007, p. 58.
[6] Yang, Qiong, et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 60.
[7] Willer, Cristen J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-169.
[8] Vasan, Ramachandran S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, 2007, p. 59.
[9] Benyamin, Beben, et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 84, no. 1, 2009, pp. 60-65.
[10] Melzer, D et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008.
[11] Pare, Guillaume, et al. “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, vol. 4, no. 7, 2008, e1000118.
[12] Yuan, X et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, vol. 83, no. 4, 2008, pp. 520–528.