Formiminoglutamate
Formiminoglutamate (FIGLU) is an organic compound that serves as an intermediate in the catabolism (breakdown) of the amino acid histidine. In the normal metabolic pathway, FIGLU is converted into glutamate and 5-formimino-tetrahydrofolate. This conversion is catalyzed by the enzyme formiminotransferase cyclodeaminase, a process that critically depends on the presence of tetrahydrofolate, a derivative of folate (vitamin B9).
Clinical Relevance
Section titled “Clinical Relevance”The concentration of FIGLU in biological fluids, particularly urine, can provide valuable insights into an individual’s nutritional status. Elevated levels of FIGLU, especially after a dietary challenge with histidine (known as a histidine load test), are a classic biochemical indicator of folate deficiency. When folate levels are insufficient, the formiminotransferase cyclodeaminase enzyme cannot function optimally, leading to the accumulation and subsequent excretion of unmetabolized FIGLU. This diagnostic approach helps identify individuals with inadequate folate stores.
Social Importance
Section titled “Social Importance”Folate is an essential vitamin crucial for numerous bodily functions, including DNA synthesis and repair, cell division, and red blood cell formation. Folate deficiency can lead to significant health problems, such as megaloblastic anemia, and is particularly concerning during pregnancy due to its strong association with neural tube defects in newborns. The detection of folate deficiency through markers like FIGLU is therefore of considerable public health importance, enabling timely nutritional interventions, dietary adjustments, and supplementation programs to prevent adverse health outcomes and promote overall well-being, especially in at-risk populations.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genome-wide association studies (GWAS) are inherently limited by their sample sizes, which can impact the statistical power required to robustly detect associations, particularly for genetic variants with smaller effect sizes. [1] This constraint may lead to the under-detection of true genetic associations, such as sex-specific effects that could be overlooked when analyses are pooled across sexes. [2] Consequently, published findings might primarily represent the strongest statistical signals, leaving numerous other contributing genetic factors uncharacterized.
Furthermore, the genomic coverage of these studies is often constrained by the specific SNP arrays used and their reliance on reference panels like HapMap for imputation, meaning some genes or regulatory regions may be missed. [2] While imputation expands the genomic scope, it introduces a degree of uncertainty, with estimated error rates for imputed alleles ranging from 1.46% to 2.14% in some analyses. [3] These limitations in SNP coverage and imputation accuracy can affect the precision of identified associations and potentially obscure the true underlying genetic architecture of complex traits. The ultimate validation of GWAS findings critically depends on replication in independent cohorts, as initial associations, especially those with modest statistical support, can sometimes be inflated or represent false positives. [4] Effect sizes are often estimated from specific stages of multi-stage studies, which can influence their interpretation and broad generalizability. [3] Without consistent replication across diverse populations, the robustness and clinical utility of identified genetic variants remain uncertain and require further confirmation.
Generalizability and Phenotypic Characterization
Section titled “Generalizability and Phenotypic Characterization”A significant limitation across many genetic studies is the predominant focus on populations of European or Caucasian ancestry. [5] This restricted demographic severely limits the generalizability of findings to other ethnic groups, where genetic architectures, allele frequencies, and patterns of linkage disequilibrium can differ substantially. As a result, the identified genetic associations might not be universally applicable, necessitating further research in more diverse populations to ensure equitable health insights and to identify population-specific genetic factors.
The accurate and consistent measurement of phenotypes is also crucial for reliable genetic association studies, yet variability can arise from several factors. For instance, levels of serum markers are known to be influenced by the time of blood collection and menopausal status, potentially introducing confounding effects if not rigorously controlled across all participants. [6] Additionally, many biological traits do not follow a normal distribution, requiring complex statistical transformations that, while necessary, can impact the direct interpretability of associations. [5] Such methodological variations and inherent complexities in phenotype assessment can introduce noise and reduce the power to detect genuine genetic signals, affecting the overall validity of the associations.
Environmental Confounders and Remaining Knowledge Gaps
Section titled “Environmental Confounders and Remaining Knowledge Gaps”While some studies attempt to account for environmental and lifestyle factors, such as smoking status, body mass index (BMI), and medication use, or perform gene-by-environment interaction analyses, it remains challenging to capture the full spectrum of non-genetic influences.[5]Unmeasured or inadequately controlled environmental confounders can obscure or falsely amplify genetic associations, leading to an incomplete understanding of the complex interplay between genes and environment. This complexity highlights the ongoing challenge of dissecting the precise contributions of genetic and environmental factors to trait variation and disease risk.
Despite the identification of numerous genetic loci associated with various traits, a substantial portion of the heritability for many complex traits often remains unexplained. [7] This “missing heritability” suggests that current GWAS methods may not fully capture all contributing genetic factors, including rare variants, structural variations, or complex epistatic interactions. Furthermore, identified genetic associations frequently require functional validation to elucidate the precise biological mechanisms through which they influence traits, representing a critical knowledge gap that needs to be addressed through further experimental and mechanistic research. [4]
Variants
Section titled “Variants”Genetic variations, known as single nucleotide polymorphisms (SNPs), play a significant role in individual differences in metabolism, development, and disease susceptibility. While many variants have broad impacts, some are directly linked to specific metabolic pathways, such as those involving formiminoglutamate (FIGLU), a key intermediate in histidine catabolism and an indicator of folate status. The presence of elevated FIGLU often points to a functional deficiency in tetrahydrofolate, essential for its conversion to glutamate.
The FTCD (Formiminotransferase Cyclodeaminase) gene, with variants like rs398124234 , encodes a crucial enzyme directly involved in the one-carbon metabolism pathway, specifically responsible for processing formiminoglutamate. This enzyme catalyzes the conversion of FIGLU to glutamate and 5,10-methenyltetrahydrofolate, a process that relies on adequate tetrahydrofolate levels. Impaired activity due to a variant inFTCDcan lead to an accumulation of FIGLU, which is often detectable in urine, especially after a histidine load, serving as a biomarker for folate status.[1] Similarly, ALDH1A1 (Aldehyde Dehydrogenase 1 Family Member A1) is vital for detoxification, oxidizing various aldehydes, including those derived from retinol, into their corresponding carboxylic acids. While not directly part of the core folate pathway, variants such as rs116866400 in ALDH1A1 can influence broader metabolic homeostasis and oxidative stress, which may indirectly affect pathways reliant on cofactors like folate. [1]
Other variants impact genes involved in cellular structure and development, which can have downstream effects on metabolic regulation. CDH2 encodes N-cadherin, a protein fundamental for cell-cell adhesion and crucial for tissue development, particularly in the nervous system and heart. A variant like rs576467 in CDH2could subtly alter cell adhesion properties, potentially affecting tissue integrity and development, which might have indirect implications for systemic metabolic processes, though a direct link to formiminoglutamate is not evident.[1] Likewise, DSCAM (Down Syndrome Cell Adhesion Molecule), with variants such as rs1312212 , is essential for neuronal development, guiding axon growth and synapse formation, and is implicated in the neurological features of Down syndrome. Meanwhile, NELL1(Neural Epidermal Growth Factor-like 1 Protein), associated with variants likers12286809 , plays a key role in bone formation and craniofacial development. While these genes are not directly linked to folate or FIGLU metabolism, their roles in fundamental developmental and physiological processes suggest that variations could influence overall health and metabolic resilience.[1]
Furthermore, genetic variations can influence gene regulation and cellular transport mechanisms, which indirectly affect metabolite availability. The region encompassing ZNF318 (Zinc Finger Protein 318), a transcription factor, and ABCC10(ATP Binding Cassette Subfamily C Member 10), a transporter protein, may contain variants likers1993655 . Alterations in ZNF318 could affect the expression of various genes, while changes in ABCC10 function, known for effluxing diverse substrates from cells, could impact cellular metabolic balance and nutrient availability, potentially affecting pathways that interact with folate. [1] Long intergenic non-protein coding RNAs (lncRNAs) such as LINC01639 (with rs117891308 ), LINC02281, LINC01551 (with rs9324105 ), and LINC02196 (with rs6880646 ) are crucial regulators of gene expression, often influencing metabolic pathways by modulating the activity of enzymes or transporters. The rs117891308 variant is also associated with SCUBE1(Signal Peptide Finally,CSMD1 (CUB and Sushi Multiple Domains 1) is a large gene implicated in complement regulation and neuronal development, and a variant like rs924737 could influence immune responses or neurological functions, with potential downstream effects on overall metabolic health and nutrient processing.
The provided research materials primarily detail genetic influences on lipid concentrations, urate transport, and markers of metabolic syndrome and diabetes. They do not contain specific information regarding the pathways and mechanisms directly involving formiminoglutamate.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs398124234 | FTCD | formiminoglutamate measurement |
| rs116866400 | ALDH1A1 | 5-hydroxylysine measurement gamma-glutamylleucine measurement formiminoglutamate measurement |
| rs117891308 | SCUBE1 - LINC01639 | picolinoylglycine measurement kynurenate measurement formiminoglutamate measurement |
| rs576467 | CDH2 | formiminoglutamate measurement |
| rs924737 | CSMD1 | formiminoglutamate measurement |
| rs12286809 | NELL1 | formiminoglutamate measurement |
| rs1993655 | ZNF318 - ABCC10 | formiminoglutamate measurement |
| rs1312212 | DSCAM | formiminoglutamate measurement |
| rs9324105 | LINC02281, LINC01551 | formiminoglutamate measurement |
| rs6880646 | LINC02196 | formiminoglutamate measurement |
References
Section titled “References”[1] Kathiresan, S. et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, no. 12, 2008, pp. 1428-37.
[2] 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, p. S12.
[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] 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, p. S10.
[5] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, p. e1000072.
[6] Benyamin, B. et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 83, no. 6, 2008, pp. 758-64.
[7] 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 Med Genet, vol. 8 Suppl 1, 2007, p. S2.