Asparagylvaline
Asparagylvaline is a dipeptide, a small molecule composed of two amino acid residues: aspartic acid and valine. These two amino acids are linked together by a peptide bond. Dipeptides are fundamental biochemical units that can be formed during the breakdown of larger proteins (proteolysis) or can serve as precursors in the synthesis of new proteins. Beyond their role as building blocks, dipeptides can also participate in various physiological processes, including nutrient transport and cellular signaling.
Biological Basis
Section titled “Biological Basis”As a component of the human metabolome, asparagylvaline is involved in the intricate network of metabolic pathways. It can originate from the digestion of dietary proteins or be synthesized endogenously within the body. The field of metabolomics focuses on the comprehensive study of these small-molecule metabolites in biological samples, such as human serum. Research in this area often explores how genetic variations can influence the levels and dynamics of such compounds.[1]
Clinical Relevance
Section titled “Clinical Relevance”Variations in the concentrations of metabolites, including dipeptides like asparagylvaline, can provide valuable insights into an individual’s metabolic health, dietary patterns, and susceptibility to certain diseases. Genetic studies, particularly genome-wide association studies (GWAS), aim to identify specific genetic factors that contribute to the observed variability in metabolite profiles. By linking genetic markers to metabolic traits, researchers can better understand the underlying biological mechanisms of health and disease.[1]
Social Importance
Section titled “Social Importance”The study of dipeptides and other metabolites holds significant promise for public health. This research contributes to advancements in personalized nutrition, enabling tailored dietary recommendations based on an individual’s unique metabolic and genetic makeup. Furthermore, understanding these molecules can lead to the discovery of new biomarkers for the early detection and diagnosis of diseases, as well as the development of more targeted therapeutic interventions. Ultimately, genetic research into metabolite profiles helps to elucidate how an individual’s inherited traits shape their metabolic landscape, influencing overall well-being and disease risk.[1]
Limitations
Section titled “Limitations”Generalizability and Cohort Specificity
Section titled “Generalizability and Cohort Specificity”A primary limitation of many genetic association studies, including those for traits like asparagylvaline, stems from the demographic characteristics of the study cohorts. Many cohorts are predominantly composed of individuals of white European descent and span a specific age range, often middle-aged to elderly.[2] This demographic homogeneity restricts the generalizability of findings to younger populations or individuals of other ethnic or racial backgrounds. While some studies employ methods to mitigate population stratification, the inherent lack of diversity in the underlying samples means that identified genetic associations may not be universally applicable across all human populations. [3]
Furthermore, the specific characteristics of study participants, such as age structure or recruitment methods, can introduce biases that affect the broader applicability of results. For example, a cohort skewed towards older individuals might reflect survival bias, where only individuals who lived long enough to participate are included. [2] Such biases can limit the ability to extrapolate findings to younger populations or those with different health profiles, potentially obscuring genetic influences that manifest differently across the lifespan or in diverse genetic backgrounds.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many genome-wide association studies (GWAS) face limitations related to sample size and statistical power, which can lead to false negative findings and an inability to detect associations with modest effect sizes.[2] The breadth of genetic coverage is also a concern; early GWAS, utilizing arrays with fewer SNPs (e.g., 100K arrays or subsets of HapMap SNPs), may miss true associations due to insufficient coverage of specific gene regions or a lack of comprehensive genotyping for candidate genes. [4] This limited coverage means that even robust findings may not fully represent the genetic architecture underlying a trait.
Replication of findings in independent cohorts remains a critical challenge, with a significant proportion of initially reported associations failing to replicate. [2] This lack of replication can arise from several factors, including false positive findings in initial studies, differences in cohort characteristics that modify genotype-phenotype associations, or insufficient statistical power in replication cohorts to detect true signals. [2] Additionally, phenotype measurement itself can introduce variability; factors like the time of day blood samples are collected or the menopausal status of participants can influence biomarker levels, potentially confounding genetic associations if not consistently controlled or accounted for in analyses. [5]
Unaccounted Factors and Remaining Knowledge Gaps
Section titled “Unaccounted Factors and Remaining Knowledge Gaps”The genetic contribution to complex traits like asparagylvaline is often influenced by a myriad of environmental factors and gene-environment interactions that are challenging to fully capture and model. Confounding variables, such as differences in blood collection protocols or other lifestyle factors, can obscure or modify the true genetic effects.[5] While some studies incorporate environmental variables into multivariate regression models, the complete spectrum of these interactions is rarely fully elucidated, leaving potential gaps in understanding the full etiology of the trait. [6]
Despite identifying significant genetic variants, a substantial portion of the heritability for complex traits often remains unexplained, a phenomenon sometimes referred to as “missing heritability.” For instance, even for well-studied traits, identified variants may only account for a fraction of the total genetic variation. [5] This indicates that many genetic determinants, including rare variants, structural variations, or complex epistatic interactions, are yet to be discovered. Furthermore, current GWAS primarily identify statistical associations, necessitating extensive functional validation and follow-up studies to understand the underlying biological mechanisms by which these genetic variants influence the trait. [2]
Variants
Section titled “Variants”The rs3733402 variant is located within the KLKB1gene, which encodes plasma kallikrein, a crucial serine protease involved in the kinin-kallikrein system. This system plays a significant role in various physiological processes, including blood pressure regulation, inflammation, coagulation, and fibrinolysis.[7]Plasma kallikrein’s primary function is to cleave high molecular weight kininogen, leading to the release of bradykinin, a potent peptide that causes vasodilation. As an intronic single nucleotide polymorphism (SNP),rs3733402 does not directly alter the amino acid sequence of the plasma kallikrein protein. However, intronic variants can exert their influence by affecting gene expression, altering messenger RNA (mRNA) splicing patterns, or impacting regulatory elements within the gene, thereby modulating the amount or activity of the resulting protein.[8]
Variations within the KLKB1 gene, such as rs3733402 , could potentially influence the efficiency of plasma kallikrein production or its overall activity. Dysregulation of plasma kallikrein activity has been implicated in a range of conditions, including hereditary angioedema, a disorder characterized by recurrent swelling episodes, and may also contribute to the risk of cardiovascular diseases, given its role in blood pressure and coagulation pathways.[6] Such genetic variations are typically identified through genome-wide association studies (GWAS), which aim to link specific genetic markers to complex traits and diseases across diverse populations. [9] Understanding how rs3733402 might alter KLKB1 function is key to unraveling its potential impact on health.
The proteolytic activity of plasma kallikrein, encoded by KLKB1, is central to its biological roles, as it involves the precise cleavage of protein substrates to release active peptides. This process of peptide processing is fundamental to amino acid metabolism and the generation of bioactive molecules. Alterations inKLKB1 activity due to variants like rs3733402 could therefore have implications for the availability or metabolism of various peptides, including dipeptides such as asparagylvaline.[10] While the direct association of rs3733402 with asparagylvaline levels requires specific investigation, its role in a major proteolytic system suggests that it could indirectly influence the broader landscape of circulating peptides and amino acid derivatives, impacting metabolic balance and potentially overlapping traits related to inflammation and vascular health.[11]
The provided research context does not contain information regarding ‘asparagylvaline’.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs3733402 | KLKB1 | IGF-1 measurement serum metabolite level BNP measurement venous thromboembolism vascular endothelial growth factor D measurement |
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Regulation of Amino Acid Homeostasis and Protein Turnover
Section titled “Regulation of Amino Acid Homeostasis and Protein Turnover”The precise regulation of amino acid concentrations is fundamental for numerous biological processes, including protein synthesis, energy metabolism, and the synthesis of other vital biomolecules. A key regulatory mechanism involves the ubiquitin-proteasome system, where proteins are marked for degradation, thereby influencing the pool of free amino acids. A polymorphism in thePARK2 gene, specifically rs992037 , has been observed to significantly alter the concentrations of several amino acids in human serum. [1] This finding suggests that PARK2, which encodes parkin, a known ubiquitin ligase, plays a crucial role in maintaining amino acid homeostasis by modulating protein turnover and degradation pathways.
Interplay with Nitrogen Metabolism: The Urea Cycle
Section titled “Interplay with Nitrogen Metabolism: The Urea Cycle”The amino acids whose concentrations are affected by genetic variants in PARK2include some that are directly involved in the urea cycle.[1]The urea cycle is a critical metabolic pathway responsible for detoxifying ammonia, a byproduct of amino acid catabolism, by converting it into urea for excretion. Perturbations in the levels of these amino acid precursors, potentially mediated byPARK2activity and subsequent protein degradation, can directly impact the flux and efficiency of the urea cycle. Such metabolic shifts underscore the intricate connection between protein quality control, amino acid metabolism, and the body’s overall nitrogen balance and waste elimination systems.
Post-Translational Control and Metabolic Flux
Section titled “Post-Translational Control and Metabolic Flux”The role of PARK2as a ubiquitin ligase highlights the profound impact of post-translational modifications on regulating metabolic pathways and cellular functions. Ubiquitination serves as a precise molecular tag, targeting specific proteins for degradation, which can directly control the abundance and activity of enzymes involved in amino acid metabolism or the availability of amino acids themselves. This sophisticated regulatory mechanism allows cells to rapidly adapt their metabolic state in response to various physiological cues and environmental changes. Consequently, dysregulation arising from genetic variants inPARK2 can disrupt the delicate balance of protein turnover, thereby influencing metabolic flux and the integrity of the cellular proteome.
Systems-Level Metabolic Integration and Disease Mechanisms
Section titled “Systems-Level Metabolic Integration and Disease Mechanisms”The implications of PARK2dysregulation extend beyond cellular metabolism, influencing systems-level integration and contributing to disease pathogenesis. Loss-of-function mutations inPARK2are recognized as a cause of Parkinson’s disease, establishing a direct link between this ubiquitin ligase and neurodegenerative disorders.[1]The observed alterations in amino acid profiles associated withPARK2polymorphisms suggest that metabolic disturbances, potentially stemming from impaired protein quality control, may represent an underlying mechanism contributing to the complex pathology of such diseases. A deeper understanding of these interconnected pathways could reveal novel therapeutic targets aimed at restoring metabolic homeostasis and mitigating disease progression.
Clinical Relevance
Section titled “Clinical Relevance”Genetic Insights and Prognostic Indicators
Section titled “Genetic Insights and Prognostic Indicators”Genome-wide association studies (GWAS) investigating various biomarker traits, including metabolites like asparagylvaline, aim to uncover genetic variants associated with their levels, which can serve as prognostic indicators for various health outcomes. Such research has demonstrated how specific biomarkers can predict disease progression and long-term implications for patient health. For instance, C-reactive protein (CRP), a marker of inflammation, has been shown to predict cardiovascular disease risk in women, highlighting the potential for genetically influenced biomarker levels to inform future health trajectories.[12]Similarly, elevated gamma-glutamyl transferase (GGT) levels have been associated with increased risk of metabolic syndrome, cardiovascular disease, and mortality, underscoring the prognostic value of certain biomarker measurements.[13]
These investigations leverage comprehensive datasets, such as those from the Framingham Heart Study, to identify robust associations after accounting for various confounding factors like age, sex, and lifestyle covariates.[2] The ultimate validation of these genetic findings and their prognostic utility relies on replication in independent cohorts and functional follow-up studies, ensuring their reliability for clinical application. [2]Understanding the genetic underpinnings of metabolite levels, like those of asparagylvaline, could thus provide valuable tools for predicting individual risk profiles and anticipating disease courses.
Diagnostic Utility and Risk Stratification
Section titled “Diagnostic Utility and Risk Stratification”The identification of genetic associations with biomarker and metabolite levels, including those that might influence asparagylvaline, offers avenues for enhanced diagnostic utility and refined risk stratification in clinical practice. By identifying individuals with specific genetic predispositions that influence biomarker concentrations, clinicians can potentially improve early risk assessment for various conditions. This personalized medicine approach allows for the identification of high-risk individuals who may benefit from targeted prevention strategies or more intensive monitoring. For example, a genetic risk score has been proposed to identify individuals with asymptomatic hyperuricemia who might warrant treatment, moving beyond current general recommendations.[14]
Such genetic insights can inform treatment selection by pinpointing individuals who may respond differently to therapies based on their biomarker profiles. The robust statistical support observed in some GWAS for associations between genes and their protein products, such as CRP gene and CRP concentration, illustrates how cis-acting regulatory variants can influence biomarker levels, providing a biological basis for personalized therapeutic strategies. [2] This integration of genetic and metabolomic data holds promise for developing more precise diagnostic criteria and tailored patient management plans.
Associations with Comorbidities and Overlapping Phenotypes
Section titled “Associations with Comorbidities and Overlapping Phenotypes”Investigations into genetic influences on biomarker and metabolite levels, including those of asparagylvaline, frequently reveal associations with various comorbidities and overlapping phenotypes, providing a more comprehensive understanding of complex disease etiologies. These studies can uncover how aberrant metabolite levels are linked to related conditions or complications, shedding light on shared biological pathways. For instance, the established association of elevated gamma-glutamyl transferase (GGT) with metabolic syndrome and cardiovascular disease illustrates how a single biomarker can be implicated in multiple interrelated health issues.[13]
Understanding these associations is crucial for recognizing syndromic presentations and developing holistic patient care strategies. The multivariable adjustment applied in these studies, accounting for factors like age, sex, body mass index, and prevalent cardiovascular disease, strengthens the validity of these observed associations, suggesting independent links rather than mere confounding.[2]By mapping the genetic architecture influencing metabolites, researchers can identify common genetic variants that contribute to polygenic dyslipidemia and other complex conditions, thereby enhancing our understanding of interconnected disease pathways.[15]
References
Section titled “References”[1] Gieger, Christian, et al. “Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum.”PLoS Genetics, vol. 4, no. 11, 2008, p. e1000282.
[2] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, 2007, p. S11.
[3] 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 Genetics, vol. 4, no. 7, 2008, e1000118.
[4] O’Donnell, Christopher J., et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Medical Genetics, vol. 8, 2007, p. S12.
[5] Benyamin, B., et al. “Variants in TF and HFEexplain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 83, no. 6, 2008, pp. 693-703.
[6] Sabatti C. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet. 2008.
[7] Yuan X. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet. 2008.
[8] Burkhardt R. “Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13.” Arterioscler Thromb Vasc Biol. 2008.
[9] Wallace C. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet. 2008.
[10] Gieger C. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet. 2009.
[11] Ober C. “Effect of variation in CHI3L1 on serum YKL-40 level, risk of asthma, and lung function.”N Engl J Med. 2008.
[12] Ridker, Paul M., et al. “C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women.”New England Journal of Medicine, vol. 342, no. 12, 2000, pp. 836-843.
[13] Lee, Do-Soo, et al. “Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham Heart Study.”Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 27, no. 1, 2007, pp. 127-133.
[14] Dehghan, Abbas, et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”The Lancet, vol. 372, no. 9654, 2008, pp. 1823-1831.
[15] Kathiresan, Sekar, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 41, no. 1, 2009, pp. 56-65.