Valylglycine
Background
Section titled “Background”Valylglycine is a dipeptide, a small molecule composed of two amino acids, valine and glycine, linked by a peptide bond. Dipeptides are fundamental building blocks of proteins and play various roles as intermediates in metabolic pathways. Valylglycine is naturally present in human serum, and its levels can be influenced by diet, physiological state, and genetic factors.[1]
Biological Basis
Section titled “Biological Basis”As a dipeptide, valylglycine serves as a product of protein degradation or an intermediate in protein synthesis. Its concentration in the bloodstream reflects ongoing metabolic processes related to protein turnover and amino acid metabolism. Metabolomics, the large-scale study of metabolites within an organism, often investigates compounds like valylglycine to understand how genetic variations might impact an individual’s unique metabolic profile. Genome-wide association studies (GWAS) examine the entire genome to identify genetic variants associated with specific metabolite levels, including valylglycine, providing insights into the genes and pathways that regulate their concentrations.[1]
Clinical Relevance
Section titled “Clinical Relevance”Variations in serum metabolite levels, including those of dipeptides like valylglycine, can serve as indicators of an individual’s metabolic health. Changes in valylglycine levels might reflect alterations in protein metabolism, nutritional status, or the activity of specific enzymatic pathways. Research into the genetic determinants of such metabolites aims to uncover biological mechanisms underlying various physiological states and disease conditions. Understanding these associations could potentially contribute to identifying biomarkers for early disease detection, monitoring disease progression, or predicting responses to therapies.
Social Importance
Section titled “Social Importance”The study of metabolites and their genetic influences holds significant promise for advancing personalized medicine. By understanding how an individual’s genetic makeup affects their metabolome, including compounds like valylglycine, it may become possible to develop more tailored diagnostic tools and interventions. This knowledge can contribute to a deeper understanding of human health and disease, offering potential for improved risk assessment, preventive strategies, and the development of targeted treatments that are customized to an individual’s unique genetic and metabolic profile.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Current genome-wide association studies (GWAS) often face challenges related to statistical power and the accurate detection of genetic variants. The inherently small effect sizes of many genetic associations with clinical phenotypes necessitate very large populations to achieve sufficient statistical power for identifying novel genetic variants. [1] This limitation means that studies with moderate sample sizes may miss important associations or provide findings that are difficult to prioritize for follow-up. [2] Furthermore, distinguishing true associations from background noise or inflated effect sizes remains a fundamental challenge, especially without external validation.
Another significant constraint pertains to SNP coverage and the reliability of replication. Initial GWAS were often based on a subset of all available SNPs, primarily from older HapMap builds, which could lead to insufficient coverage in certain gene regions and potentially miss true genetic associations. [3] While imputation methods can infer missing genotypes, these processes introduce a degree of error, with reported rates of 1.46% to 2.14% per allele [4] which may affect the accuracy of findings. The ultimate validation of associations critically depends on replication in independent cohorts; however, non-replication can occur due to the complexity of linkage disequilibrium, where different associated SNPs might be in strong linkage with an unknown causal variant, or due to multiple causal variants within the same gene region. [5] Moreover, effect sizes and statistical significance can vary between discovery and replication cohorts, with individual SNPs sometimes failing to reach significance in replication despite larger effect sizes, underscoring the complexities of consistent validation. [6]
Population Specificity and Generalizability
Section titled “Population Specificity and Generalizability”A prominent limitation of many genetic association studies is their predominant focus on populations of European ancestry, which restricts the generalizability of findings. Numerous studies and their replication cohorts explicitly state that participants were of European, Caucasian, or white European ancestry. [7] Even when efforts are made to control for population stratification through methods like principal component analysis, these analyses are often conducted within a largely homogeneous Caucasian group. [6] This narrow demographic focus means that genetic associations identified may not be directly applicable or share the same effect sizes in other ancestral groups due to differences in allele frequencies, genetic architecture, and environmental exposures.
The limited diversity in study populations also impacts the broader applicability of findings to global populations. Studies conducted in founder populations, such as those in Kosrae, Micronesia, can offer unique insights due to reduced genetic heterogeneity but may not easily generalize to more admixed populations. [8] While some research has attempted to extend findings to multiethnic cohorts [9] the initial discovery and primary validation stages frequently remain concentrated on a single ancestral group. This reliance on a limited range of ancestral backgrounds means that a comprehensive understanding of genetic influences across the full spectrum of human diversity remains an ongoing challenge, leaving open questions about the transferability of genetic risk factors and therapeutic implications.
Phenotypic Characterization and Unmeasured Confounding
Section titled “Phenotypic Characterization and Unmeasured Confounding”The accurate characterization and measurement of phenotypes, along with the management of confounding factors, represent significant challenges in genetic studies. Many biological phenotypes, such as protein levels, exhibit non-normal distributions, requiring various statistical transformations (e.g., log, Box-Cox) to approximate normality for analysis. [7] These transformations, while necessary, can complicate the interpretation and comparison of results across different studies. Additionally, strict exclusion criteria, such as removing individuals on lipid-lowering therapies [9]limit the applicability of findings to specific, often treatment-naïve, subpopulations rather than the broader demographic. While studies incorporate adjustments for common covariates like age, sex, and body mass index[6] the selection of a specific genetic model, such as an additive one, may overlook non-additive genetic effects, potentially missing relevant associations. [6]
Despite extensive efforts in genetic analysis and statistical adjustment, a substantial portion of the heritability for many complex traits remains unexplained, a phenomenon often referred to as “missing heritability.” Current GWAS approaches may not fully capture the intricate interplay between multiple genetic variants, rare variants, epigenetic factors, or complex gene-environment interactions. The relatively small effect sizes observed for genetic associations with clinical outcomes [1] suggest that a comprehensive understanding requires delving into intermediate phenotypes or accounting for unmeasured environmental factors. This gap implies that while identified genetic loci contribute to trait variation, a complete picture of the genetic and environmental architecture influencing such phenotypes necessitates continued research to uncover additional genetic determinants and their contextual influences. [10]
Variants
Section titled “Variants”Genetic variations play a crucial role in individual differences in metabolism, cellular signaling, and overall physiological health. The identified variants span a diverse set of genes and genomic regions, each contributing to fundamental biological processes that could influence or be influenced by the presence and metabolism of small peptides like valylglycine. Valylglycine, as a dipeptide, is subject to enzymatic breakdown and can participate in various physiological functions, including serving as a nutrient, signaling molecule, or precursor for other compounds, with its precise role being sensitive to the broader metabolic landscape.
The CNDP2(Carnosine Dipeptidase 2) gene encodes a metallopeptidase known for its role in hydrolyzing dipeptides, particularly carnosine, in various tissues including the kidney and liver. Variantsrs2278161 , rs2278159 , and rs734559 within CNDP2 could affect the enzyme’s activity or expression, thereby modulating the efficiency of dipeptide breakdown. A similar role is attributed to TMPRSS9(Transmembrane Serine Protease 9), wherers17685098 may impact its proteolytic function. These enzymes are critical for processing peptides, and altered activity could directly influence the systemic levels and bioavailability of small peptides such as valylglycine. Variations in these genes may therefore contribute to individual differences in metabolic health or kidney function, traits frequently investigated in genetic association studies.[11] Changes in dipeptide metabolism orchestrated by CNDP2 or TMPRSS9could further impact amino acid availability and influence pathways related to lipid levels or uric acid regulation.[12]
Other genes implicated by identified variants include those involved in cellular signaling and metabolic regulation. The proto-oncogene KRAS, represented by rs16928693 , is a critical component of the Ras signaling pathway, regulating cell growth, differentiation, and survival. Variations in KRAScould subtly alter cellular responsiveness to environmental cues, thereby affecting nutrient metabolism and energy homeostasis, which might indirectly impact the demand for or utilization of specific amino acid compounds like valylglycine. Similarly,OSMR (Oncostatin M Receptor), with variant rs10472312 , mediates the signaling of Oncostatin M, a cytokine involved in inflammation and tissue regeneration. Modulations inOSMR signaling could influence inflammatory responses and overall metabolic balance, potentially affecting the synthesis or breakdown of circulating peptides. [13] Additionally, LFNG (LFNG O-Fucosylpeptide 3-Beta-N-Acetylglucosaminyltransferase) with rs57203146 , is involved in the glycosylation of Notch receptors, a pathway fundamental to cell fate and development. Altered glycosylation due to this variant might affect protein function, impacting metabolic pathways and the body’s interaction with nutrient signals, including those derived from amino acids and small peptides. [9] The effects of these variants on complex traits highlight the broad genetic architecture influencing human health. [7]
The remaining variants are found in genes or genomic regions with diverse cellular functions. TMC8 (Transmembrane Channel-Like 8), identified by rs139555116 , and ANKS3 (Ankyrin Repeat And Kinase Domain Containing 3), by rs1684617 , contribute to transmembrane transport and signal transduction, respectively. While a direct link to valylglycine is not immediately apparent, these genes play roles in fundamental cellular processes that collectively underpin metabolic health.DSCAML1 (Down Syndrome Cell Adhesion Molecule Like 1), with rs116904534 , and CNTN5 (Contactin 5), part of the LINC02713 - CNTN5 region with rs68044352 , are primarily involved in neural development and cell adhesion. Although seemingly distant from metabolism, neuroendocrine regulation is crucial for systemic metabolic control, and variations here could subtly alter these integrated systems. The genomic region CTB-1I21.1 (rs35377913 ) may harbor uncharacterized regulatory elements or genes that contribute to complex traits. Collectively, these genetic variations may influence various aspects of nutrient handling, metabolic signaling, and overall physiological set points, potentially affecting the intricate balance of amino acids and peptides like valylglycine in the body, which can be observed in studies on various biomarkers and quantitative traits.[14]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs2278161 rs2278159 rs734559 | CNDP2 | valylglycine measurement gamma-glutamyl-2-aminobutyrate measurement leucylglycine measurement peptide measurement |
| rs139555116 | TMC8 | valylglycine measurement |
| rs1684617 | ANKS3 | valylglycine measurement |
| rs57203146 | LFNG | valylglycine measurement |
| rs116904534 | DSCAML1 | valylglycine measurement |
| rs16928693 | KRAS - RNU4-67P | valylglycine measurement |
| rs35377913 | CTB-1I21.1 | valylglycine measurement |
| rs68044352 | LINC02713 - CNTN5 | valylglycine measurement |
| rs10472312 | OSMR | valylglycine measurement |
| rs17685098 | TMPRSS9 | valylglycine measurement |
Biological Background
Section titled “Biological Background”Genetic Regulation and Expression Patterns
Section titled “Genetic Regulation and Expression Patterns”Genetic mechanisms underpin a wide array of biological functions, dictating cellular processes and organismal traits. For instance, zinc-finger proteins, such as those encoded by a gene on chromosome 2p15, play crucial roles in gene regulation, influencing processes like F cell production. [15]Variations within genes, such as common single nucleotide polymorphisms (SNPs), can profoundly impact gene function and expression patterns. For example, SNPs inHMGCR, a gene vital for cholesterol synthesis, have been linked to alternative splicing of exon 13, which can alter HMGCR activity and subsequently affect LDL-cholesterol levels. [8] Similarly, the SLC2A9gene, encoding a urate transporter, and theGLUT9gene, which also facilitates urate transport, show associations between genetic variants and serum urate concentrations, withGLUT9 also exhibiting alternative splicing that influences protein trafficking and tissue-specific expression in the liver and kidney, notably being upregulated in diabetic states [16], [17], [18]. [19] These genetic variations contribute to the complex interplay determining physiological phenotypes and susceptibility to various conditions [1]. [6]
Metabolic Pathways and Molecular Transport
Section titled “Metabolic Pathways and Molecular Transport”Metabolic pathways are intricate networks of biochemical reactions essential for life, often regulated by key enzymes and transport proteins. The mevalonate pathway, responsible for cholesterol biosynthesis, is critically regulated by the enzyme 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR). [8] Lipid metabolism further involves the synthesis of long-chain poly-unsaturated fatty acids from essential precursors like linoleic acid, a process dependent on enzymes such as fatty acid desaturase 1 (FADS1). [1] Cellular transport mechanisms are equally vital, with proteins like SLC2A9 and GLUT9playing a central role in the renal excretion and reabsorption of uric acid, thereby modulating serum urate levels[16], [17], [18]. [19]Beyond urate,GLUT9has also been implicated in glucose transport and metabolism, potentially influencing lactate and other organic anion levels.[17] Furthermore, the zinc transporter ZnT-8is specifically expressed in pancreatic beta-cells, where it localizes to insulin secretory granules and is crucial for glucose-induced insulin secretion, highlighting its role in glycemic control.[6]
Cellular Signaling and Intercellular Interactions
Section titled “Cellular Signaling and Intercellular Interactions”Cellular signaling pathways enable cells to communicate and respond to their environment, often involving receptors and adhesion molecules that mediate interactions. The intercellular adhesion molecule-1 (ICAM-1), for instance, is a critical component in cell-cell interactions, binding to integrins like Mac-1 (CD11b/CD18) and playing a role in immune responses and inflammation. [6] The signaling activity of soluble ICAM-1 can be enhanced by specific glycosylation patterns, such as sialylated complex-type N-glycans, which highlights the importance of post-translational modifications in modulating protein function. [6] Interestingly, an association has been observed between the ABO histo-blood group antigen and soluble ICAM-1 levels, suggesting a potential link between blood group genetics and inflammatory or vascular processes. [6] Other signaling cascades, such as the mitogen-activated protein kinase (MAPK) pathway, are activated in response to various stimuli, impacting cellular growth, differentiation, and stress responses, while components of the 5’-AMP-activated protein kinase (AMPK) pathway, like PRKAG2, are key regulators of energy homeostasis. [20]
Physiological Homeostasis and Disease Implications
Section titled “Physiological Homeostasis and Disease Implications”Disruptions in physiological homeostasis can lead to various pathophysiological conditions, affecting multiple tissues and organ systems. High serum uric acid levels, influenced by genetic variants in transporters likeSLC2A9 and GLUT9, are a major risk factor for gout, a painful inflammatory arthritis[16], [17], [18]. [19] Similarly, dysregulation of lipid metabolism, often stemming from variations in genes such as HMGCR, APOC3, and MLXIPL, can lead to unfavorable plasma lipid profiles, including altered LDL-cholesterol and triglyceride levels, increasing the risk for cardiovascular diseases[8], [21], [22]. [23] At the organ level, cardiac function and morphology, measured by echocardiographic dimensions, can be influenced by genetic factors affecting key cardiac transcription factors like MEF2C, which plays a role in cardiac morphogenesis and can contribute to conditions like dilated cardiomyopathy.[20]Moreover, metabolic indicators like glycated hemoglobin, a marker of long-term glucose control, are influenced by genes such asHK1 and zinc transporters, reflecting their systemic impact on conditions like type 2 diabetes. [6]
There is no information about ‘valylglycine’ in the provided context to construct a “Pathways and Mechanisms” section.
Clinical Relevance
Section titled “Clinical Relevance”Genetic Influence on Uric Acid Homeostasis
Section titled “Genetic Influence on Uric Acid Homeostasis”A common nonsynonymous variant, Val253Ile (rs16890979 ) in the GLUT9gene, is strongly associated with serum uric acid levels.[19]Individuals carrying the Ile allele typically exhibit lower uric acid concentrations, an association observed across diverse populations including Old Order Amish, Sardinian, and Chianti cohorts.[19]This genetic influence on uric acid appears to be independent of other known cardiovascular risk factors and estimated glomerular filtration rate (eGFR).[19] The consistent genetic association highlights the critical role of GLUT9in regulating human uric acid transport and maintaining its systemic balance.
Implications for Hyperuricemia and Related Conditions
Section titled “Implications for Hyperuricemia and Related Conditions”The Val253Ilevariant carries indirect prognostic value due to its effect on serum uric acid, a biomarker associated with various health issues. Elevated uric acid (hyperuricemia) is a known risk factor for gout and has been linked to cardiovascular inflammation and several metabolic traits, including percent body fat, triglycerides, HDL, LDL, glucose, and insulin.[19] Although the GLUT9variant itself was not consistently associated with these cardiovascular and metabolic traits directly in studies, its significant influence on uric acid levels suggests it could modulate an individual’s predisposition to conditions where uric acid plays a pathogenic role. The presence of the Ile allele, by lowering uric acid, might thus contribute to a reduced risk for such complications and influence long-term health outcomes.
Personalized Risk Assessment and Monitoring Strategies
Section titled “Personalized Risk Assessment and Monitoring Strategies”Genotyping for the Val253Ile variant in GLUT9could inform personalized medicine approaches for risk stratification related to uric acid-associated conditions. Identifying individuals with the Ile allele, who are genetically predisposed to lower uric acid levels, may help refine their risk assessment for developing hyperuricemia and conditions like gout.[19]Furthermore, studies have revealed sex-specific effects, with the Ile allele exhibiting a more pronounced uric acid-lowering effect in premenopausal women compared to men and postmenopausal women.[19]This sex- and potentially age-dependent genetic influence underscores the necessity for tailored risk evaluations and could guide targeted monitoring strategies, enabling clinicians to identify high-risk individuals for earlier intervention or more personalized management of uric acid levels.
References
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[3] 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. S11.
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[6] Pare, G. 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, 2008.
[7] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.
[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] 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. 189-197.
[10] O’Donnell, C. J., et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S3.
[11] Hwang, Shih-Jen, et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007.
[12] Wallace, Chris, et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”American Journal of Human Genetics, vol. 82, no. 1, 2008, pp. 139-149.
[13] Ridker, Paul M., et al. “Loci related to metabolic-syndrome pathways including LEPR, HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women’s Genome Health Study.”American Journal of Human Genetics, vol. 82, no. 5, 2008, pp. 1185-1192.
[14] Saxena, Richa, et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331-1336.
[15] Menzel, S. et al. “A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p15.” Nat Genet, 2007.
[16] Doring, A. et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, 2008.
[17] Li, S. et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, 2007.
[18] Vitart, V. et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, 2008.
[19] McArdle, P. F. et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.”Arthritis Rheum, 2008.
[20] 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, 2007.
[21] Kooner, J. S. et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, 2008.
[22] Pollin, T. I. et al. “A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection.” Science, 2008.
[23] Hiura, Y. et al. “Identification of genetic markers associated with high-density lipoprotein-cholesterol by genome-wide screening in a Japanese population: the Suita study.”Circ J, 2009.