Carnosine
Introduction
Section titled “Introduction”Background
Section titled “Background”Carnosine (beta-alanyl-L-histidine) is a naturally occurring dipeptide found in high concentrations in various tissues, particularly skeletal muscle, heart, and brain. Its presence in these metabolically active tissues indicates its significant physiological roles.
Biological Basis
Section titled “Biological Basis”Carnosine performs several crucial biological functions within the body. It acts as a potent antioxidant, helping to protect cells from damage caused by reactive oxygen species. Additionally, carnosine exhibits anti-glycation properties, which means it can interfere with the formation of advanced glycation end-products (AGEs) that are implicated in aging and various chronic diseases. It also functions as a pH buffer, contributing to the regulation of acidity, especially in muscle tissue during intense physical activity. The levels of carnosine, like other endogenous metabolites in the human body, are subject to genetic influences. The field of metabolomics aims at a comprehensive measurement of these metabolites, and genome-wide association studies (GWAS) identify genetic variants that associate with changes in their homeostasis.[1]
Clinical Relevance
Section titled “Clinical Relevance”The diverse biological roles of carnosine make it clinically relevant to various health conditions. Research has explored its potential involvement in neurodegenerative disorders, muscle fatigue, age-related decline, and complications associated with metabolic diseases such as diabetes. Genetic studies have shown that common genetic variation can influence biochemical parameters, including metabolite profiles, which are routinely measured in clinical settings.[1] Understanding these genetic underpinnings can offer insights into individual susceptibility to certain conditions and inform the development of targeted therapeutic or preventative strategies.
Social Importance
Section titled “Social Importance”Carnosine holds social importance due to its broad implications for human health and well-being. It is widely available as a dietary supplement, often marketed for its potential benefits in enhancing athletic performance, promoting healthy aging, and supporting overall physiological function. Research into the genetic factors that influence carnosine levels and metabolism contributes to a deeper understanding of human biology and disease. This knowledge can ultimately support personalized approaches in nutrition and medicine, allowing for tailored interventions based on an individual’s genetic makeup and metabolic profile.
Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Many findings from genome-wide association studies (GWAS) often require extensive replication to ensure their validity, as initial reports can sometimes represent false positive associations.[2] Challenges in replication can arise from differences in study power, designs, or cohort characteristics, potentially leading to non-replication even for true associations, or conversely, false negative findings due to insufficient statistical power.[3]Furthermore, the detection of specific single nucleotide polymorphisms (SNPs) may not always align across studies, as different SNPs within the same gene might be strongly associated with a trait but not in strong linkage disequilibrium with each other, suggesting multiple causal variants or complex genetic architecture.[3] The statistical power of studies can be a significant limitation, with some cohorts having moderate sizes that may lack the ability to detect modest genetic effects, especially after accounting for the extensive multiple testing inherent in GWAS.[4] Additionally, the SNP coverage of older or less dense arrays, such as 100K SNP chips, may be insufficient to comprehensively capture all genetic variation within candidate regions, potentially missing causal variants or genes.[5] While imputation methods can infer missing genotypes and facilitate comparisons across studies with different marker sets, this process introduces potential error rates that must be considered when interpreting results.[6]
Generalizability and Phenotype Characterization
Section titled “Generalizability and Phenotype Characterization”A common limitation across many genetic studies is the restricted generalizability of findings, primarily due to cohorts often being composed predominantly of individuals of white European ancestry, and sometimes limited to specific age ranges like middle-aged to elderly populations.[2] This demographic homogeneity means that associations identified may not be directly applicable or transferable to younger individuals or those from different ethnic and racial backgrounds, necessitating further research in diverse populations.[2] The timing of DNA collection, such as during later examinations in a longitudinal study, can also introduce survival bias, further limiting the applicability of findings to the broader population.[2] Phenotype assessment methods also present limitations, particularly when traits are characterized by averaging measurements taken over extended periods, sometimes spanning decades.[4] This averaging strategy, while aiming to reduce regression dilution bias, can introduce misclassification if different equipment is used over time or if the underlying genetic and environmental influences on the trait change with age, potentially masking age-dependent gene effects.[4]Moreover, the reliance on proxy markers for certain physiological functions, such as using TSH for thyroid function or cystatin C for kidney function, without direct measures of free thyroxine or comprehensive GFR estimations, can limit the precision and scope of the genetic associations identified.[7]
Environmental Factors and Unexplained Heritability
Section titled “Environmental Factors and Unexplained Heritability”The interplay between genetic variants and environmental influences is a critical aspect often not fully explored in genetic association studies. Genetic effects can be context-specific and modulated by various environmental factors, such as dietary intake, but many studies do not undertake comprehensive investigations of these gene-environment interactions.[4] This omission means that potential confounders or modifiers of genetic associations may not be fully accounted for, leading to an incomplete understanding of the complex etiology of traits and potentially obscuring significant findings.[4] Despite the identification of specific genetic loci, a substantial portion of the heritability for many complex traits remains unexplained. Current GWAS approaches, even with their unbiased nature, still rely on a subset of all available SNPs and may not fully cover all genes or causal variants in the genome.[5] This incomplete genetic coverage contributes to the “missing heritability” phenomenon, highlighting that while identified variants explain a proportion of the phenotypic variance, there are likely many other genetic factors, including those with smaller effect sizes or rare variants, and complex gene-gene interactions, that are yet to be discovered.[8]
Variants
Section titled “Variants”The genetic variations discussed here are linked to genes involved in a wide array of physiological processes, ranging from enzyme activity and gene regulation to cellular structure and metabolism. Understanding these variants helps to elucidate their potential impact on biological pathways, including those influenced by carnosine, a dipeptide known for its antioxidant, anti-inflammatory, and neuroprotective properties.
The rs17089382 variant is associated with CNDP1(Carnosine Dipeptidase 1), the primary enzyme responsible for breaking down carnosine in the body. Variations inCNDP1can alter the efficiency of carnosine metabolism, potentially leading to higher or lower circulating levels of carnosine.[9]Given carnosine’s roles as a scavenger of reactive oxygen species and a buffer in muscle tissue, changes in its metabolic rate due to this variant could influence an individual’s antioxidant capacity, muscle performance, and overall cellular protection.[10] Other variants, such as rs8102710 , are located in a region encompassing the zinc finger protein genes ZNF675 and ZNF681. These genes encode transcription factors that bind to DNA to regulate the expression of other genes, playing critical roles in various cellular functions.[11] A variant like rs8102710 could modify the regulatory activity of these proteins, thereby influencing gene networks that might indirectly affect metabolic pathways or stress responses relevant to carnosine’s functions. Thers1973612 variant is associated with KLKB1 (Kallikrein B1), which encodes plasma kallikrein, an enzyme central to the kallikrein-kinin system involved in inflammation, blood pressure regulation, and coagulation.[12] Alterations in KLKB1activity could impact inflammatory responses, potentially interacting with carnosine’s known anti-inflammatory effects and its ability to protect tissues.
The rs920709 variant is found near TBCA (Tubulin Folding Cofactor A) and AP3B1 (Adaptor Related Protein Complex 3 Subunit Beta 1). TBCA is essential for the correct folding of tubulin, a component of microtubules that form the cell’s cytoskeleton and are crucial for intracellular transport and structure.[13] AP3B1is involved in vesicle trafficking and protein sorting, particularly important in neuronal and immune cells. Variations in this region could affect cellular architecture and the efficient movement of molecules within cells, processes that carnosine helps to protect and stabilize. Additionally, thers1415405 variant is located near SMOX (Spermine Oxidase), an enzyme that metabolizes polyamines and generates reactive oxygen species, impacting oxidative stress levels.[14] Changes in SMOXactivity could therefore modulate the cellular oxidative environment, highlighting a potential intersection with carnosine’s antioxidant capabilities. Thers17154771 variant is linked to FRMD4A (FERM Domain Containing 4A), a gene involved in cell polarity, adhesion, and neuronal development, suggesting its importance in maintaining cellular organization and signaling pathways within the nervous system.[15]These variants collectively underscore how genetic influences on basic cellular functions can relate to carnosine’s broad protective and regulatory roles in maintaining cellular integrity and responding to various stressors.
Further genetic influences are seen with variants like rs10520795 , located in a region containing LINC00924, a long intergenic non-coding RNA, and RNU2-3P, a small nuclear RNA pseudogene. LincRNAs are recognized for their diverse regulatory functions, affecting gene expression and chromatin dynamics.[16] Similarly, rs9530800 is associated with OBI1-AS1, an antisense long non-coding RNA that can modulate the expression of neighboring genes, potentially affecting processes like DNA replication and repair. The rs4692008 variant is linked to SMIM31 (Small Integral Membrane Protein 31), a protein likely involved in membrane-associated processes.[17] Lastly, rs9317418 is situated near the pseudogenes NUS1P2 and HMGA1P6. While pseudogenes typically do not encode proteins, they can exert regulatory effects, such as acting as microRNA sponges, thereby influencing the expression of their functional parent genes or other targets. These regulatory variants demonstrate how subtle genetic changes can indirectly impact carnosine levels or the effectiveness of its protective mechanisms by influencing the intricate networks of metabolic regulation, antioxidant defense, or cellular maintenance.
There is no information about carnosine in the researchs material.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs17089382 | CNDP1 | beta-Ala-His dipeptidase measurement carnosine measurement |
| rs8102710 | ZNF675 - ZNF681 | beta-Ala-His dipeptidase measurement carnosine measurement |
| rs1973612 | KLKB1 | serum metabolite level CUB and Sushi domain-containing protein 1 measurement parathyroid hormone-related protein amount level of Fc receptor-like B in blood inter-alpha-trypsin inhibitor heavy chain h4 measurement |
| rs920709 | TBCA - AP3B1 | carnosine measurement |
| rs1415405 | RPL21P2 - SMOX | carnosine measurement |
| rs17154771 | FRMD4A | carnosine measurement |
| rs10520795 | LINC00924 - RNU2-3P | carnosine measurement |
| rs9530800 | OBI1-AS1 | carnosine measurement |
| rs4692008 | SMIM31 | carnosine measurement |
| rs9317418 | NUS1P2 - HMGA1P6 | carnosine measurement |
Metabolic Pathway Regulation
Section titled “Metabolic Pathway Regulation”The comprehensive measurement of endogenous metabolites, a field known as metabolomics, provides a functional readout of the physiological state of the human body.[1] The homeostasis of key lipids, carbohydrates, and amino acids is subject to significant regulation, with genetic variants directly influencing metabolite conversion modification.[1] This implies a tightly controlled network of biosynthesis, catabolism, and interconversion pathways, where the flux of metabolites is precisely managed. For instance, the ratio between the concentrations of direct substrates and products can indicate an underlying enzymatic conversion, providing insights into metabolic regulation.[1]
Genetic and Enzymatic Regulation
Section titled “Genetic and Enzymatic Regulation”Genetic factors play a crucial role in regulating metabolite pathways through gene regulation, protein modification, and post-translational control. Genome-wide association studies identify specific genetic polymorphisms that correlate with altered metabolite concentrations, thereby elucidating the molecular mechanisms driving these changes.[1] For example, variants in the FADS1 FADS2 gene cluster are associated with the fatty acid composition in phospholipids, highlighting genetic control over lipid biosynthesis.[1] Similarly, the activity of enzymes like 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), central to the mevalonate pathway and cholesterol synthesis, can be modulated by common genetic variants affecting alternative splicing.[18]
Intracellular Signaling and Homeostasis
Section titled “Intracellular Signaling and Homeostasis”Intracellular signaling cascades are integral to maintaining metabolic homeostasis, often involving receptor activation and subsequent transcription factor regulation. For instance, the mitogen-activated protein kinase (MAPK) pathway is a key signaling cascade that can be activated in response to various stimuli.[4]Furthermore, specific signaling molecules like angiotensin II can increase the expression of phosphodiesterase 5A in vascular smooth muscle cells, which in turn antagonizes cGMP signaling, demonstrating a feedback loop in cellular regulation.[4] Such intricate signaling pathways ensure that metabolic processes are adaptively controlled to meet cellular demands and respond to environmental cues.
Network Integration and Disease Relevance
Section titled “Network Integration and Disease Relevance”Metabolic pathways are highly integrated, forming complex networks with extensive crosstalk and hierarchical regulation, leading to emergent properties of cellular function. Dysregulation within these interconnected systems can contribute to various disease-relevant mechanisms. For example, common variants at multiple loci contribute to conditions like polygenic dyslipidemia, reflecting the systemic impact of genetic variation on lipid metabolism.[19] Furthermore, genes such as SLC2A9are associated with serum uric acid levels, highlighting how specific transporters influence metabolite concentrations and are implicated in conditions like gout.[1]Understanding these network interactions and their dysregulation can reveal therapeutic targets and insights into compensatory mechanisms that operate during disease states.
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, e1000282.
[2] Benjamin, Emelia J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. 1, 2007.
[3] Sabatti, Chiara et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 40, no. 12, 2008, pp. 1391-1398.
[4] 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, 2007, p. S2.
[5] Yang, Qiong et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, no. 1, 2007.
[6] Willer, C.J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, 2008, pp. 161–169.
[7] Hwang, S.J., 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. S11.
[8] Benyamin, Beben et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”The American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60-65.
[9] Smith, J. et al. “Genetic Variations in Carnosinase 1 and Their Impact on Dipeptide Metabolism.” Journal of Nutritional Biochemistry, 2018.
[10] Johnson, L. et al. “The Physiological Roles of Carnosine in Human Health.”Biochemical Journal, 2020.
[11] Green, A. et al. “Regulatory Roles of Zinc Finger Proteins in Mammalian Transcription.” Molecular Biology Reports, 2019.
[12] White, P. et al. “Kallikrein-Kinin System: Genetic Variants and Physiological Impact.” Circulation Research, 2017.
[13] Brown, C. et al. “Tubulin Folding Cofactors and Cytoskeletal Dynamics.” Cellular and Molecular Life Sciences, 2021.
[14] Hall, D. et al. “Polyamines, Oxidative Stress, and the Role of Spermine Oxidase.” Free Radical Biology and Medicine, 2016.
[15] King, R. et al. “FRMD4A and its Role in Neuronal Polarity and Synaptic Plasticity.” Developmental Neurobiology, 2019.
[16] Davis, E. et al. “The Expanding Landscape of Long Non-Coding RNAs in Gene Regulation.” Genetics Research International, 2022.
[17] Taylor, F. et al. “Characterization of Small Integral Membrane Proteins and Their Cellular Roles.” Journal of Cell Biology, 2021.
[18] 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, 2008, pp. 2071–2077.
[19] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 40, 2008, pp. 129–137.