Asparagylleucine

Introduction

Background

Asparagylleucine is a hypothetical small molecule, or metabolite, that plays a role in human metabolism. Metabolites are the small molecules that are the intermediates and end products of metabolic processes, and their comprehensive measurement in biological fluids like serum is the focus of metabolomics, a rapidly evolving field.^[1] The study of how genetic variations influence the levels of such metabolites provides insights into the physiological state of the human body.

Biological Basis

The biological basis of asparagylleucine levels, like many other metabolites, is thought to be influenced by an interplay of genetic and environmental factors. Genetic variants, often identified through genome-wide association studies (GWAS), can associate with changes in the homeostasis of key biological compounds, including amino acids.^[1]These genetic associations help illuminate the biochemical pathways involved in a metabolite’s synthesis, degradation, or transport. For instance, research has explored genetic variations influencing a wide range of metabolite profiles in human serum.^[1]

Clinical Relevance

Understanding the genetic determinants of asparagylleucine levels could have significant clinical relevance. Alterations in metabolite profiles are often implicated in various health conditions. For example, genetic loci have been identified that influence the levels of other metabolites such as uric acid, lipids (e.g., triglycerides, HDL cholesterol), and liver enzymes, all of which are biomarkers for cardiovascular disease and other metabolic traits.^[2]Similarly, variations in asparagylleucine levels, if found to be genetically influenced, could serve as biomarkers or indicate predisposition to certain diseases.

Social Importance

The social importance of studying metabolites like asparagylleucine lies in its potential to advance personalized medicine and public health. By identifying common genetic variants that influence metabolite levels, researchers can better understand disease mechanisms and identify individuals at higher risk for certain conditions. This knowledge could inform lifestyle recommendations, targeted interventions, or the development of new therapeutic strategies based on an individual’s genetic profile and metabolic characteristics. The broader impact includes improving disease prevention and promoting healthier aging.

Limitations

Constraints in Study Design and Statistical Interpretation

Studies investigating traits like asparagylleucine often face limitations related to their design and statistical power. Many cohorts are of moderate size, leading to inadequate statistical power and an increased susceptibility to false negative findings . The proper functioning ofKLKB1is essential for maintaining cardiovascular homeostasis and modulating the body’s response to injury and infection.^[3]

The single nucleotide polymorphism (SNP)rs3733402 is located within the KLKB1gene. Depending on its specific position and the resulting nucleotide change, this variant can influence gene expression, protein stability, or enzymatic activity of plasma kallikrein. For instance, variants in coding regions might alter the amino acid sequence, potentially affecting the enzyme’s catalytic efficiency or its interaction with substrates like HMWK. Alternatively, variants in non-coding regions, such as introns or regulatory sequences, could impact the transcription or splicing of theKLKB1 mRNA, thereby modulating the overall levels of plasma kallikrein protein. Such alterations in kallikrein activity can, in turn, affect the production of bradykinin and other downstream signaling molecules. ^[4] This subtle genetic variation can thus contribute to individual differences in inflammatory responses and vascular tone. ^[3]

The implications of rs3733402 for asparagylleucine, a hypothetical peptide or metabolic marker, would likely stem from its role in protein processing and amino acid metabolism. Given that plasma kallikrein is a protease, altered activity due tors3733402 could indirectly affect the availability or breakdown of peptides, potentially influencing the levels or processing of asparagylleucine-containing molecules. For example, changes in the kinin-kallikrein system, driven byKLKB1variants, can impact inflammatory states, which in turn affect overall cellular metabolism and protein turnover. Such systemic changes could influence the pathways involved in the synthesis or degradation of dipeptides or amino acid derivatives, including asparagylleucine. Therefore,rs3733402 might be associated with variations in asparagylleucine levels through its broad influence on proteolytic cascades and metabolic regulation.^[4] This interplay highlights the complex connections between genetic variants, enzymatic activity, and the broader metabolic landscape, where even small changes in a key protease can have far-reaching effects on various biomolecules. ^[3]

History and Epidemiology

Early Recognition and Genetic Dissection of Metabolic Traits

The scientific understanding of complex metabolic traits, such as asparagylleucine, has profoundly evolved with the advent of genome-wide association studies (GWAS).^[5] Prior to these large-scale genomic efforts, the genetic underpinnings of such traits were often inferred from family studies or candidate gene approaches, yielding limited comprehensive insights. Landmark studies published in the late 2000s began to systematically identify specific genetic loci associated with various metabolic profiles in human serum, marking a pivotal shift in scientific methodology. ^[5]

These key discoveries involved analyzing hundreds of thousands of single nucleotide polymorphisms (SNPs) across diverse populations to pinpoint regions of the genome influencing traits like lipid levels, liver enzymes, and other metabolic biomarkers.^[6] This approach allowed for the identification of numerous common variants contributing to polygenic dyslipidemia and other metabolic variations, thereby advancing the mechanistic understanding beyond simple Mendelian inheritance patterns. ^[7] The collaborative nature of these studies, involving multiple European and international cohorts, was crucial in validating findings and enhancing the statistical power to detect subtle genetic effects. ^[6]

Global and Demographic Patterns of Metabolic Variation

Epidemiological studies on metabolic traits, potentially including asparagylleucine, have primarily focused on populations of European ancestry, with significant contributions from cohorts across the United Kingdom, Finland, Sweden, Germany, Croatia, and Italy.^[6] Research efforts have extended to specific founder populations, such as those in Oulu, Finland, and Sardinia, Italy, which offer unique genetic structures for identifying variants influencing metabolic profiles. ^[8]These studies, often leveraging large-scale population data, have provided insights into the geographic distribution of genetic predispositions to various metabolic conditions, though global prevalence rates and incidence for specific metabolites like asparagylleucine are not uniformly detailed across all ancestral groups.^[6]

Demographic analyses within these cohorts have explored the influence of factors such as age, sex, and ancestry on metabolic trait levels. For instance, age and sex are frequently included as covariates in statistical models to adjust for their known effects on phenotypes, including serum uric acid levels and lipid concentrations.^[6]While the studies highlight the critical role of genetic variation, they also implicitly acknowledge the interplay of environmental and lifestyle factors which, alongside demographic characteristics, contribute to the observed variability in metabolic profiles across different segments of the population.^[5]

Evolving Epidemiological Landscape and Future Directions

The epidemiological landscape for understanding complex metabolic traits has been transformed by the identification of numerous genetic loci, providing a foundation for tracking changing patterns and cohort effects. While specific secular trends for asparagylleucine are not explicitly detailed, the identification of genetic variants influencing lipid levels, liver enzymes, and other metabolic indicators allows for a more refined understanding of how these traits manifest across different generations and environments.^[6] These genetic insights enable researchers to explore how gene-environment interactions contribute to the evolving epidemiology of metabolic disorders over time, moving beyond solely observational studies. ^[5]

Future epidemiological projections for metabolic traits are increasingly informed by these genetic discoveries, particularly in identifying individuals at higher risk for conditions like dyslipidemia and coronary artery disease.^[7]The ongoing collection of data from large cohorts, such as the Framingham Heart Study, will continue to refine our understanding of how genetic predispositions interact with an aging global population and changing lifestyles.^[9] Such comprehensive genetic and epidemiological data are crucial for developing targeted public health interventions and personalized medicine approaches to manage and prevent metabolic imbalances. ^[6]

Biological Background

Genetic Regulation and Gene Expression

Genetic variations play a fundamental role in shaping molecular and cellular pathways, often through their influence on gene expression and function. For instance, common single nucleotide polymorphisms (SNPs) within theHMGCR gene are associated with varying levels of LDL-cholesterol. ^[10] These genetic differences can critically impact the alternative splicing of exon 13 in HMGCR, a sophisticated regulatory mechanism that allows a single gene to produce multiple protein isoforms with distinct functions. ^[10] Alternative splicing itself is a complex regulatory network involving numerous control mechanisms, and its dysregulation is increasingly recognized in human diseases. ^[11] Similarly, the alternative splicing of APOB mRNA, which can be induced by antisense oligonucleotides, leads to the generation of novel APOB isoforms, further illustrating the diverse ways gene products can be modulated at the post-transcriptional level. ^[12]

Beyond splicing, gene function is also controlled by transcription factors, which are proteins that regulate the rate of gene transcription. The BCL11A gene, for example, encodes the BCL11AXL transcription factor, which is distributed across various normal and malignant tissues and plays a crucial role in gene regulation. ^[13]Such transcription factors are integral to developmental processes, influencing outcomes like persistent fetal hemoglobin production and even ameliorating conditions such as beta-thalassemia.^[13] The precise control exerted by these genetic mechanisms and regulatory networks ultimately dictates the cellular functions and physiological characteristics of an organism.

Metabolic Pathways and Lipid Homeostasis

The maintenance of metabolic balance, particularly lipid homeostasis, is a complex process involving a myriad of biomolecules and enzymatic reactions. Genome-wide association studies (GWAS) have revealed that genetic variations influence the profiles of various metabolites in human serum, including different classes of lipids such as diacyl, acyl-alkyl, and dialkyl glycerols, as well as plasmalogen/plasmenogen phosphatidylcholines. ^[5] The specific composition of fatty acid side chains, characterized by their carbon length and number of double bonds (e.g., Cx:y), is a critical determinant of lipid structure and function, impacting membrane fluidity and signaling. ^[5]

Central to cholesterol biosynthesis is the enzyme HMG-CoA reductase, whose activity and degradation are under strict regulatory control, influenced by its oligomerization state. ^[14] Genetic variants in genes like MLXIPLare associated with plasma triglyceride levels, underscoring the genetic underpinnings of lipid concentrations and the risk of dyslipidemia.^[15] Moreover, common genetic variants located at numerous loci collectively contribute to polygenic dyslipidemia, a complex condition characterized by abnormal lipid levels that can lead to systemic consequences. ^[7] The FADS1/FADS2 gene cluster also harbors genetic variants associated with the fatty acid composition in phospholipids, demonstrating how genetic factors dictate the molecular makeup of essential cellular lipids. ^[5] Disruptions in fatty acid metabolism, such as those caused by defects in medium-chain acyl-CoA dehydrogenase encoded by ACADM, can lead to severe metabolic disorders identified through newborn screening. ^[5]

Cellular Transport and Uric Acid Metabolism

Cellular transport mechanisms are essential for maintaining metabolic balance and preventing the accumulation of harmful substances. The SLC2A9gene encodes a critical urate transporter that significantly influences serum urate concentration and its excretion from the body.^[16]This transporter is crucial for maintaining uric acid homeostasis, and its activity exhibits pronounced sex-specific effects, highlighting how biological sex can modulate metabolic regulation.^[16]

Pathophysiological processes arise when these homeostatic mechanisms are disrupted; for instance, impaired SLC2A9function can lead to the accumulation of uric acid, resulting in conditions like gout.^[17] The proper functioning of such cellular transporters is vital for overall cellular health and the prevention of metabolic diseases. The intricate interplay between genetic predispositions, the efficiency of these transport proteins, and other environmental factors collectively determines an individual’s susceptibility to diseases stemming from metabolic imbalances.

Organ-Specific Effects and Systemic Disorders

Genetic variations and metabolic disruptions often manifest with specific effects at the tissue and organ level, leading to systemic consequences. Genetic loci have been identified that influence plasma levels of liver enzymes, such as alkaline phosphatase, which is regulated by genes likeAkp2 and serves as an indicator of liver function. ^[18]In conditions like nonalcoholic fatty liver disease, biomolecules such as glycosylphosphatidylinositol-specific phospholipase d play a role, illustrating the complex tissue interactions and molecular pathways involved in liver health.^[18]

Beyond hepatic function, genetic factors profoundly impact hematological phenotypes. Quantitative trait loci (QTLs) have been mapped to genes encoding zinc-finger proteins, which are involved in regulating F cell production.^[19] The BCL11Agene, through its role in producing a transcription factor, is associated with persistent fetal hemoglobin levels, a condition that can ameliorate the clinical phenotype of beta-thalassemia, demonstrating a significant systemic impact on blood disorders.^[13] Furthermore, genetic variations, such as those near MC4R, are linked to broader metabolic parameters like waist circumference and insulin resistance, indicating their contribution to systemic conditions like metabolic syndrome and increasing the risk of coronary artery disease.^[20]

Pathways and Mechanisms

The biological activity and homeostatic regulation of asparagylleucine are intricately linked to a network of metabolic, signaling, and regulatory pathways. Research in metabolomics and genetics reveals how genetic variants can influence the concentrations of various metabolites, including amino acids, lipids, and carbohydrates, thereby shedding light on the broader molecular mechanisms that likely govern compounds like asparagylleucine. These pathways operate in a highly integrated manner, influencing cellular function and contributing to overall physiological states.

Metabolic Homeostasis and Nutrient Flux

The regulation of asparagylleucine is fundamentally tied to core metabolic pathways responsible for the biosynthesis, catabolism, and interconversion of amino acids, lipids, and carbohydrates. For instance, the urea cycle is a critical pathway for amino acid metabolism, processing excess nitrogen from amino acid breakdown.^[5] Similarly, fatty acid metabolism, influenced by gene clusters like FADS1 and FADS2, dictates the composition of essential polyunsaturated fatty acids, which are vital structural and signaling molecules. ^[21]Dysregulation in these pathways can lead to altered metabolite profiles, potentially impacting the availability or processing of asparagylleucine and contributing to conditions such as dyslipidemia.^[7]The transport of metabolites, such as uric acid and fructose bySLC2A9 (GLUT9) and other solute carriers, further highlights the importance of controlled flux for maintaining cellular and systemic balance. ^[22]

Genetic and Post-Translational Regulatory Mechanisms

The cellular concentration and activity of molecules like asparagylleucine are under precise regulatory control at multiple levels, from gene expression to protein function. Gene regulation, often mediated by transcription factors such asBCL11A, dictates the synthesis rates of enzymes and transporters involved in metabolic pathways, thereby influencing overall metabolite levels. ^[13] Beyond transcriptional control, post-translational modifications are crucial for fine-tuning protein activity and stability; for example, ubiquitin ligases like parkin, encoded by PARK2, regulate protein degradation, which can impact the abundance of specific metabolic enzymes or structural proteins. ^[5] Furthermore, alternative splicing, as observed for genes like HMGCR (affecting cholesterol levels) and APOB (involved in lipid transport), generates diverse protein isoforms from a single gene, adding another layer of regulatory complexity that can alter metabolic pathway components and their functions. ^[10]

Interconnected Signaling and Network Dynamics

The pathways influencing asparagylleucine are not isolated but form an interconnected network where crosstalk and hierarchical regulation are fundamental to systemic homeostasis. Alterations in one metabolic pathway, such as lipid metabolism, can ripple through others, affecting amino acid profiles or energy balance.^[5] For instance, genetic variants impacting a single gene like FADS1 can have widespread effects on multiple fatty acid metabolites, illustrating the emergent properties of these complex biological networks. ^[5] The concerted action of genes like CPN1, ERLIN1, SAMM50, and PNPLA3, which are involved in diverse liver functions including protein processing, ER organization, mitochondrial import, and phospholipase activity, demonstrates how a coordinated network of cellular components maintains physiological balance and can impact overall metabolic health. ^[18]

Clinical Relevance and Therapeutic Interventions

Understanding the pathways and mechanisms governing metabolites like asparagylleucine provides crucial insights into disease pathogenesis and potential therapeutic strategies. Dysregulation in metabolic pathways, such as those involving lipids or uric acid, is directly implicated in common conditions like dyslipidemia and gout.^[17]Similarly, disruptions in amino acid metabolism, potentially influenced by genes likePARK2, have been linked to neurological disorders such as Parkinson’s disease, while other metabolic changes are associated with type 2 diabetes.^[5] By identifying specific genes and mechanisms involved in metabolite homeostasis, researchers can pinpoint therapeutic targets to correct pathway dysregulation and develop interventions that restore balance, thereby addressing the underlying causes of various metabolic and systemic disorders.

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

References

[1] Gieger, Christian, et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genetics, vol. 5, no. 11, 2009, e1000694.

[2] Dehghan, Abbas, et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, vol. 372, no. 9654, 2008, pp. 1858-1864.

[3] GeneCards Human Gene Database. KLKB1. 2023.

[4] National Center for Biotechnology Information. KLKB1 Gene Information. 2023.

[5] 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, p. e1000282.

[6] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 41, no. 1, 2009, p. 47-55.

[7] Kathiresan S, et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.

[8] Li, S., et al. “The GLUT9 gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, vol. 3, no. 11, 2007, e194.

[9] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 62.

[10] 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, vol. 28, no. 11, 2008, pp. 2078-85.

[11] Caceres, J. F., and A. R. Kornblihtt. “Alternative splicing: multiple control mechanisms and involvement in human disease.”Trends Genet, vol. 18, no. 4, 2002, pp. 186–193.

[12] Khoo, B. et al. “Antisense oligonucleotide-induced alternative splicing of the APOB mRNA generates a novel isoform of APOB.” BMC Mol Biol, vol. 8, no. 1, 2007, p. 3.

[13] Uda M, et al. “Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia.”Proc Natl Acad Sci U S A, vol. 105, no. 5, 2008, pp. 1620-5.

[14] Cheng, H. H. et al. “Oligomerization state influences the degradation rate of 3-hydroxy-3-methylglutaryl-CoA reductase.” J Biol Chem, vol. 274, no. 24, 1999, pp. 17171–17178.

[15] Kooner, J. S. et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, vol. 40, no. 2, 2008, pp. 149–151.

[16] Doring, A. et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430–436.

[17] Vitart V, et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, vol. 40, no. 4, 2008, pp. 437-42.

[18] 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. 520-8.

[19] Menzel, S. et al. “A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p15.” Nat Genet, vol. 39, no. 9, 2007, pp. 1197–1199.

[20] Chambers, J. C. et al. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nat Genet, vol. 40, no. 5, 2008, pp. 718–720.

[21] Schaeffer L, et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet, vol. 15, no. 10, 2006, pp. 1745-56.

[22] McArdle PF, et al. “Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.”Arthritis Rheum, vol. 58, no. 8, 2008, pp. 2894-901.