Indolepropionylglycine
Indolepropionylglycine (IPG) is a biochemical compound classified as an indole derivative and a metabolite of tryptophan, an essential amino acid. It is primarily generated through the metabolic activity of gut microbiota, which convert tryptophan into indolepropionic acid (IPA). Subsequently, IPA is conjugated with glycine to form IPG, a process often occurring in the liver or kidneys, which typically facilitates the excretion of various compounds from the body.
Biological Basis
Section titled “Biological Basis”As a metabolite, indolepropionylglycine’s presence and concentration in human serum are indicative of both host metabolic processes and the activity of the gut microbiome. Indolepropionic acid (IPA), its precursor, is recognized for its antioxidant properties and its role as a ligand for the pregnane X receptor (PXR), influencing various detoxification and metabolic pathways. Therefore, IPG levels can indirectly reflect these broader physiological functions. The field of metabolomics, which involves the comprehensive measurement of endogenous metabolites, considers IPG as a functional readout of the physiological state. [1] Genetic variants can influence the homeostasis of such metabolites, including IPG, thereby impacting an individual’s metabolic profile. [1]
Clinical Relevance
Section titled “Clinical Relevance”Fluctuations in indolepropionylglycine levels can be clinically relevant, often serving as a biomarker for gut microbiome health and its systemic implications. Imbalances in gut microbiota (dysbiosis) can alter the production of indole metabolites, including IPG. Due to its link to IPA, IPG may be implicated in conditions associated with oxidative stress, inflammation, and metabolic disorders such as type 2 diabetes and cardiovascular disease. Understanding the genetic determinants of IPG levels, as explored in genome-wide association studies, could provide insights into disease susceptibility and progression.
Social Importance
Section titled “Social Importance”The study of metabolites like indolepropionylglycine holds significant social importance, particularly in the context of personalized medicine and public health. By identifying genetic influences on IPG levels, researchers can better understand individual variations in metabolic responses to diet and environmental factors. This knowledge may inform targeted dietary or probiotic interventions aimed at optimizing gut microbiota function and overall metabolic health, potentially contributing to strategies for preventing or managing chronic diseases.
Variants
Section titled “Variants”The regulation of metabolic processes, including the breakdown of amino acids and fatty acids, is influenced by genetic variations that can affect enzyme activity and overall physiological balance. Two such notable genetic variants are rs1047891 in the CPS1 gene and rs1251075 in the ACADMgene. These single nucleotide polymorphisms (SNPs) are situated within genes central to critical energy and detoxification pathways, and their impact can extend to the availability of precursors for various metabolites, including those derived from tryptophan like indolepropionylglycine. Understanding these variants helps to elucidate individual differences in metabolic responses and health outcomes.[1]
The rs1047891 variant is found within the CPS1gene, which encodes Carbamoyl Phosphate Synthetase 1. This enzyme plays a fundamental role in the urea cycle, catalyzing the rate-limiting step that converts ammonia and bicarbonate into carbamoyl phosphate. The urea cycle is essential for detoxifying ammonia, a byproduct of protein and amino acid metabolism, into urea for excretion.[2] Variations in CPS1, such as rs1047891 , can influence the efficiency of this enzyme, potentially affecting an individual’s capacity to process nitrogenous waste and impacting plasma amino acid concentrations. Alterations in amino acid metabolism, particularly those of tryptophan, could consequently modulate the synthesis or degradation pathways of indolepropionylglycine, a metabolite implicated in various physiological processes. Furthermore, the broader metabolic effects ofCPS1 variants may interact with other pathways, influencing overall metabolic health and an individual’s metabolic profile. [1]
Another important genetic variant is rs1251075 , located within the ACADM gene, which codes for Medium-chain acyl-CoA dehydrogenase (MCAD). MCAD is a crucial mitochondrial enzyme responsible for the beta-oxidation of medium-chain fatty acids, a process vital for energy production, especially during fasting or periods of increased energy demand. [1] Variants in ACADMcan lead to reduced MCAD enzyme activity, impairing the body’s ability to utilize medium-chain fats for energy and causing an accumulation of medium-chain acylcarnitines and other related metabolites. Such disruptions in fatty acid metabolism can have systemic effects, altering energy homeostasis and potentially impacting other interconnected metabolic pathways, including those involved in amino acid processing. The altered metabolic landscape resulting fromACADMvariants could therefore indirectly influence the intricate balance of metabolites like indolepropionylglycine, which are sensitive to shifts in overall cellular energy and nutrient availability.[3]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs1047891 | CPS1 | platelet count erythrocyte volume homocysteine measurement chronic kidney disease, serum creatinine amount circulating fibrinogen levels |
| rs1251075 | ACADM | indolepropionylglycine measurement X-18921 measurement urinary metabolite measurement |
Biological Background
Section titled “Biological Background”Metabolic Regulation and Molecular Pathways
Section titled “Metabolic Regulation and Molecular Pathways”The physiological state of the human body is fundamentally dependent on the precise regulation and homeostasis of endogenous metabolites, including key lipids, carbohydrates, and amino acids. [1] These metabolites are influenced by intricate molecular and cellular pathways, where enzymes are crucial for both their synthesis and degradation. A prime example is the mevalonate pathway, which is essential for cholesterol biosynthesis and is tightly regulated by enzymes like 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), whose activity and degradation rate are carefully controlled. [4]Furthermore, cellular functions and overall metabolic profiles are modulated by various signaling pathways, including those involving receptors such as the thyroid hormone receptor.[5]
Key biomolecules, such as Insulin-Like Growth Factor Binding Proteins and apolipoproteins likeapo CIII, are integral to lipid transport and metabolism. [6] For instance, alterations in apo CIII levels can impact the fractional catabolic rate of very low-density lipoproteins, leading to conditions like hypertriglyceridemia. [7] Specialized membrane transporters, such as SLC2A9 (also known as GLUT9), play a vital role in maintaining metabolite balance by influencing the serum concentration and excretion of substances like uric acid.[8] These molecular mechanisms collectively ensure the maintenance of metabolic equilibrium and influence various cellular processes.
Genetic Influences on Metabolite and Protein Homeostasis
Section titled “Genetic Influences on Metabolite and Protein Homeostasis”Genetic mechanisms, encompassing gene functions, regulatory elements, and gene expression patterns, significantly influence the levels of both metabolites and proteins in the body. Single nucleotide polymorphisms (SNPs), common genetic variants, can affect how genes are expressed and how critical biomolecules are produced and function.[2] A notable example is how SNPs near the HMGCR gene can alter the alternative splicing of its messenger RNA (mRNA), resulting in changes to the encoded protein and impacting LDL-cholesterol levels. [4]This process of alternative splicing is a fundamental regulatory mechanism that increases the functional diversity of gene products and has implications for human disease.[9]
Beyond specific gene expression, genomic regions known as quantitative trait loci (QTLs) and protein quantitative trait loci (pQTLs) identify genetic variations that influence the concentrations of proteins or metabolites. [10]Identifying these genetic variants is crucial for understanding disease etiology, as it can clarify whether altered protein levels are a cause or a consequence of disease processes.[2] For instance, specific variations within the FADS1 FADS2 gene cluster are associated with the composition of fatty acids in phospholipids, demonstrating the genetic control over intricate lipid profiles. [1] The substantial impact of common genetic variation on protein levels, sometimes even involving null alleles, highlights the profound role of genetics in shaping an individual’s biochemical landscape. [2]
Tissue-Specific Roles and Systemic Integration
Section titled “Tissue-Specific Roles and Systemic Integration”Biological processes extend beyond individual cells, integrating into complex tissue and organ-level functions that have systemic consequences for metabolite homeostasis. The liver serves as a primary metabolic hub, where enzymes like alkaline phosphatase (encoded by theAkp2gene) and pathways involving glycosylphosphatidylinositol-specific phospholipase D contribute to its normal function and are implicated in conditions such as nonalcoholic fatty liver disease.[5] These organ-specific effects highlight how localized metabolic processes can have widespread implications for overall health.
Maintaining systemic balance requires the coordinated activity of various tissues. Disruptions, such as those observed in dyslipidemia or hypertriglyceridemia, often arise from intricate interactions between an individual’s genetic makeup and their metabolic pathways. [11]The concentrations of many serum and plasma proteins, which serve diverse biological roles, fluctuate with disease status, affecting metabolic, cardiovascular, inflammatory, and infectious conditions.[2] Comprehending these inter-tissue communications and systemic responses is essential for understanding how metabolic alterations manifest throughout the body and contribute to the development of complex traits and diseases. [2]
Pathophysiological Implications of Metabolic Dysregulation
Section titled “Pathophysiological Implications of Metabolic Dysregulation”Dysregulation of metabolic homeostasis can lead to various pathophysiological processes and disease states. Metabolic disorders like type 2 diabetes and dyslipidemia, characterized by abnormal triglyceride or cholesterol levels, are strongly linked to genetic variations that influence lipid metabolism.[6] For example, variation in the MLXIPLgene has been associated with plasma triglyceride levels, whileANGPTL4is a potent factor inducing hyperlipidemia and inhibiting lipoprotein lipase, an enzyme critical for breaking down lipids.[12]
Furthermore, imbalances in metabolite transport and excretion can trigger specific diseases. Elevated concentrations of serum uric acid, influenced by the urate transporterSLC2A9, can lead to gout, a condition marked by painful joint inflammation.[8]These instances underscore how genetic variants, even seemingly minor substitutions like valine to isoleucine, can alter protein structure and function, leading to clinically significant phenotypes.[13] The complex interplay among genetic susceptibility, environmental factors, and compensatory biological responses often dictates the onset and severity of these complex metabolic diseases. [13]
Clinical Relevance
Section titled “Clinical Relevance”Genetic Determinants and Metabolic Health
Section titled “Genetic Determinants and Metabolic Health”The study of protein quantitative trait loci (pQTLs) elucidates how genetic variations influence the circulating levels of various proteins, including potentially ‘indolepropionylglycine’.[2]Understanding these genetic determinants is crucial as protein levels are central to physiological processes and can contribute to metabolic health or disease risk. For instance, the adjustments made for Body Mass Index (BMI), diabetes, and lipid-lowering treatment in pQTL investigations suggest that these metabolic factors are important covariates or potential consequences in the context of protein level variations, highlighting a complex interplay between genetics, protein expression, and metabolic profiles.[2] This perspective is vital for identifying individuals with genetically predisposed altered protein levels that may contribute to metabolic dysregulation.
Associations with Chronic Disease and Risk Factors
Section titled “Associations with Chronic Disease and Risk Factors”Variations in protein levels, such as those of ‘indolepropionylglycine’, can be clinically relevant through their associations with common chronic conditions. In studies identifying pQTLs, significant clinical factors like myocardial infarction, diabetes, and hypertension are often considered, both as characteristics of the study population and as covariates in statistical models.[2]This approach recognizes that protein levels may be influenced by or contribute to the development or progression of cardiovascular and metabolic diseases. Furthermore, the inclusion of factors such as current smoking status and the use of steroids in statistical adjustments underscores their recognized role as risk factors that could modulate protein concentrations, necessitating their consideration when evaluating the clinical impact of specific protein levels.[2]
Prognostic Utility and Personalized Interventions
Section titled “Prognostic Utility and Personalized Interventions”The insights gained from investigating protein levels and their genetic regulators offer potential for prognostic applications and personalized medicine. If specific pQTLs influencing ‘indolepropionylglycine’ levels were identified, these could serve as early indicators for predicting the likelihood of disease development or progression, particularly for conditions frequently observed in study populations like the InCHIANTI cohort.[2] Such information might support personalized risk stratification, allowing for targeted prevention strategies or more precise monitoring in individuals identified as high-risk based on their genetic and protein profiles. This comprehensive understanding could ultimately inform treatment selection and lead to more effective, individualized patient care. [2]
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, 2008.
[2] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[3] Wallace, C. et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, 2008.
[4] Burkhardt, R., et al. “Common SNPs in HMGCR in Micronesians and Whites Associated with LDL-Cholesterol Levels Affect Alternative Splicing of Exon13.” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 28, no. 12, 2008, pp. 2276–2282.
[5] Yuan, Xuan, et al. “Population-Based Genome-Wide Association Studies Reveal Six Loci Influencing Plasma Levels of Liver Enzymes.” American Journal of Human Genetics, vol. 83, no. 4, 2008, pp. 520–528.
[6] Saxena, R., et al. “Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels.”Science, vol. 316, no. 5829, 2007, pp. 1331–1336.
[7] Aalto-Setala, K., et al. “Mechanism of Hypertriglyceridemia in Human Apolipoprotein (Apo) CIII Transgenic Mice. Diminished Very Low Density Lipoprotein Fractional Catabolic Rate Associated with Increased Apo CIII and Reduced Apo E on the Particles.”Journal of Clinical Investigation, vol. 90, no. 5, 1992, pp. 1889–1900.
[8] Vitart, V., et al. “SLC2A9 Is a Newly Identified Urate Transporter Influencing Serum Urate Concentration, Urate Excretion and Gout.”Nature Genetics, vol. 40, no. 4, 2008, pp. 430–436.
[9] Caceres, Jose F., and Alberto R. Kornblihtt. “Alternative Splicing: Multiple Control Mechanisms and Involvement in Human Disease.”Trends in Genetics, vol. 18, no. 4, 2002, pp. 186–193.
[10] Menzel, S., et al. “A QTL Influencing F Cell Production Maps to a Gene Encoding a Zinc-Finger Protein on Chromosome 2p15.” Nature Genetics, vol. 39, no. 10, 2007, pp. 1197–1199.
[11] Kathiresan, S., et al. “Common Variants at 30 Loci Contribute to Polygenic Dyslipidemia.” Nature Genetics, vol. 41, no. 1, 2009, pp. 56–65.
[12] Kooner, J. S., et al. “Genome-Wide Scan Identifies Variation in MLXIPL Associated with Plasma Triglycerides.” Nature Genetics, vol. 40, no. 2, 2008, pp. 149–151.
[13] McArdle, Patrick F., et al. “Association of a Common Nonsynonymous Variant in GLUT9 with Serum Uric Acid Levels in Old Order Amish.”Arthritis & Rheumatism, vol. 58, no. 10, 2008, pp. 3291–3298.