Indoleacetoylcarnitine
Introduction
Section titled “Introduction”Indoleacetoylcarnitine is a member of the acylcarnitine family, which are critical metabolites involved in the transport and metabolism of fatty acids within the body. These compounds play a fundamental role in energy production, particularly in allowing fatty acids to enter mitochondria for beta-oxidation, the process that breaks down fatty acids into energy.[1]
Biological Basis
Section titled “Biological Basis”The biosynthesis of acylcarnitines involves the enzymatic transfer of acyl groups from acyl-Coenzyme A (acyl-CoA) molecules to carnitine. This process is facilitated by carnitine acyltransferases, enabling fatty acids to cross the mitochondrial membrane. Defects in enzymes that initiate the beta-oxidation of fatty acids, such as short-chain acyl-Coenzyme A dehydrogenase (SCAD) and medium-chain acyl-Coenzyme A dehydrogenase (MCAD), have been linked to changes in the ratios of specific short and medium-chain acylcarnitines, respectively. [1]Indoleacetoylcarnitine, specifically, represents a conjugate of carnitine with indoleacetic acid. Indoleacetic acid is a compound often derived from the metabolism of the amino acid tryptophan, with gut microbiota contributing significantly to tryptophan catabolism.
Clinical Relevance
Section titled “Clinical Relevance”Variations in metabolite profiles, including acylcarnitines, are increasingly recognized as indicators of an individual’s physiological state and potential disease risk. Genome-wide association studies (GWAS) have successfully identified genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, and amino acids.[1] For instance, specific genetic polymorphisms have been associated with varying levels of acylcarnitines, highlighting a genetic influence on metabolic pathways. [1] Imbalances in acylcarnitine levels can be indicative of underlying metabolic disorders, such as fatty acid oxidation defects, which can have significant health consequences. Research efforts using metabolomics, a field focused on the comprehensive measurement of endogenous metabolites, provide a functional readout of the human body’s physiological state and are instrumental in uncovering these genetic-metabolic links. [1]
Social Importance
Section titled “Social Importance”The understanding of indoleacetoylcarnitine and other metabolites holds significant social importance by advancing precision medicine and public health initiatives. By identifying genetic predispositions that influence metabolite levels, researchers can develop better diagnostic tools, risk prediction models, and targeted interventions for metabolic diseases, including those related to fatty acid metabolism, obesity, and cardiovascular conditions.[2]This knowledge contributes to a more personalized approach to healthcare, allowing for early detection and tailored treatments that consider an individual’s unique genetic and metabolic profile. Furthermore, exploring the genetic and environmental factors affecting metabolites like indoleacetoylcarnitine can shed light on the complex interactions that underpin human health and disease.
Limitations
Section titled “Limitations”Methodological Heterogeneity and Statistical Power
Section titled “Methodological Heterogeneity and Statistical Power”Studies investigating quantitative traits like indoleacetoylcarnitine levels often face challenges stemming from varied study designs and genotyping methodologies across different cohorts. The use of diverse platforms and imputation strategies can introduce variability and potential inaccuracies, as seen in studies where different marker sets and imputation methods led to varying error rates when comparing imputed and experimentally derived genotypes.[3] While meta-analyses are employed to increase statistical power, inconsistencies in genotype quality control and imputation (e.g., lack of high-quality imputation or use of specific HapMap phases) across contributing studies can compromise the integrity and comparability of results. [2] Furthermore, disparate sample sizes across discovery and replication cohorts can influence the ability to detect true associations and reduce the overall power, especially for variants with smaller effect sizes, making the robust identification of genetic loci challenging.
Despite efforts to control for potential statistical inflation using genomic control methods, the presence of such inflation (e.g., lambda values slightly above 1) in some cohorts, particularly family-based studies, indicates that residual confounding or population structure can still impact the validity of p-values. [4] Such issues necessitate careful interpretation of association signals, as not all statistically significant findings may represent true biological relationships. Moreover, the fundamental challenge in genome-wide association studies (GWAS) is prioritizing true positive genetic associations from a large number of observed signals, and the ultimate validation of any findings requires consistent replication in independent cohorts to ensure robustness. [5] Absence of replication in some instances can be attributed to differences in linkage disequilibrium patterns between diverse ancestral populations, further complicating the generalization of findings.
Generalizability and Phenotype Definition
Section titled “Generalizability and Phenotype Definition”A significant limitation in understanding the genetic influences on indoleacetoylcarnitine levels lies in the generalizability of findings, primarily due to the ancestry composition of studied cohorts. Many large-scale GWAS efforts have predominantly focused on populations of European ancestry, with replication attempts sometimes extending to multi-ethnic samples.[6] While this has advanced understanding, it limits the direct applicability of identified genetic variants and their effect sizes to other global populations, where genetic architecture and environmental exposures may differ substantially. [2] Consequently, an observed genetic association in one population might not replicate in another, highlighting the necessity for broader population representation in future studies.
Phenotype measurement and definition also pose considerable challenges to the consistency and interpretation of results. Even with advanced targeted quantitative metabolomics platforms, like electrospray ionization (ESI) tandem mass spectrometry, used to measure acylcarnitines, variations in assay methodologies across different research centers can introduce systematic biases. [1] Furthermore, inconsistencies in handling specific aspects of the phenotype, such as the exclusion of outliers, adjustment for covariates like age squared, or the availability of information on confounding factors (e.g., lipid-lowering therapies), can lead to heterogeneity in reported effect estimates and impact the validity of meta-analyses. [6] The sensitivity of p-values to different covariates and exclusion criteria further underscores the importance of standardized phenotyping protocols. [7]
Environmental Confounding and Remaining Genetic Complexity
Section titled “Environmental Confounding and Remaining Genetic Complexity”The genetic associations identified for indoleacetoylcarnitine levels are susceptible to confounding by various environmental and lifestyle factors, which are often difficult to comprehensively capture and adjust for across studies. While common confounders such as age, gender, smoking status, and alcohol intake are frequently accounted for, the completeness and consistency of these adjustments can vary between cohorts.[7]Factors like diet, physical activity, and specific medication use (e.g., lipid-lowering treatments or steroids) are known to significantly influence metabolite levels, and their inconsistent assessment or adjustment can obscure true genetic effects or lead to spurious associations.[7] Such unmeasured or residual confounding makes it challenging to disentangle the direct genetic contributions from complex gene-environment interactions.
Even with robust statistical approaches, a substantial portion of the heritability of complex traits often remains unexplained by identified common genetic variants, a phenomenon known as “missing heritability.” This suggests that many genetic influences on indoleacetoylcarnitine levels might stem from rare variants, gene-gene interactions, or epigenetic factors that are not fully captured by current GWAS designs focusing on common single nucleotide polymorphisms (SNPs).[6]The current findings represent a step towards understanding the genetic landscape of indoleacetoylcarnitine, but they underscore the need for further research, including studies employing whole-genome sequencing, fine-mapping, and functional analyses, to elucidate the complete genetic architecture and the biological mechanisms underlying these associations.
Variants
Section titled “Variants”The regulation of indoleacetoylcarnitine, a complex metabolite, involves several genetic loci that play roles in diverse metabolic pathways, from fatty acid synthesis and degradation to mitochondrial function and solute transport. Variants within genes likeACSM2A, ACSM5, THEM4, SLC16A9, and LYRM1can influence the intricate balance of carnitine metabolism and acyl-CoA pools, thereby potentially impacting the levels and functions of indoleacetoylcarnitine. Indoleacetoylcarnitine, an acylcarnitine, is formed by the conjugation of indoleacetic acid with carnitine, and its levels can reflect both gut microbial activity and host metabolic processing, particularly within mitochondrial pathways.
The acyl-CoA synthetase medium-chain family, including ACSM2A and ACSM5, plays a crucial role in activating medium-chain fatty acids into their acyl-CoA esters, a prerequisite for their subsequent metabolic processing, such as beta-oxidation or conjugation. Variants such as rs9924150 , rs6497490 , and rs10163426 in ACSM2A, and rs12924989 in ACSM5, may alter the enzymatic efficiency or substrate specificity of these synthetases. Such changes could lead to shifts in the availability of various acyl-CoAs or free carnitine, indirectly affecting the formation or degradation of other acylcarnitines, including indoleacetoylcarnitine. Fluctuations in acylcarnitine profiles are recognized as indicators of metabolic health and have been linked to genetic variations affecting fatty acid metabolism.[1] These genes contribute to the broader landscape of metabolite regulation in human serum. [1]
An intergenic variant, rs369344065 , located between ACSM2A and ACSM2B, may influence the expression of one or both of these neighboring acyl-CoA synthetase genes. Intergenic regions often contain regulatory elements like enhancers or promoters that modulate gene transcription. Altered expression of ACSM2A or ACSM2Bcould modify the overall capacity for fatty acid activation and carnitine conjugation within cells, impacting the pool of available acyl-CoAs and carnitine. This, in turn, could lead to changes in the formation or turnover of indoleacetoylcarnitine, which, as an acylcarnitine, is part of the broader metabolic network involving fatty acid transport and oxidation.[1]The precise impact of such regulatory variants on specific acylcarnitine species like indoleacetoylcarnitine highlights the complexity of metabolic genetics.
Other genes implicated in indoleacetoylcarnitine metabolism includeTHEM4 and SLC16A9. THEM4 (Thioesterase superfamily member 4) encodes a mitochondrial thioesterase, an enzyme that hydrolyzes acyl-CoA esters, releasing free fatty acids and CoASH. A variant like rs28415528 in THEM4could affect its enzymatic activity, thereby altering the balance of acyl-CoA pools and potentially influencing the availability of substrates for carnitine acylation or the overall mitochondrial metabolic flux.SLC16A9 (Solute Carrier Family 16 Member 9) functions as a monocarboxylate transporter, mediating the transport of various organic acids across cell membranes. The variant rs1171617 in SLC16A9might affect its transport efficiency or substrate specificity, which could indirectly impact the cellular concentrations of metabolic intermediates that are precursors or byproducts of indoleacetoylcarnitine metabolism.[1] Such transporters are crucial for maintaining cellular metabolic homeostasis. [1]
Finally, LYRM1 (LYR Motif Containing 1) is involved in the assembly and function of mitochondrial respiratory chain complexes. Mitochondria are central to energy production and the metabolism of fatty acids and acylcarnitines. A variant like rs111794350 in LYRM1could impair mitochondrial efficiency or integrity, leading to a cascade of metabolic consequences that might include altered acylcarnitine profiles, such as indoleacetoylcarnitine. Efficient mitochondrial function is essential for the beta-oxidation of fatty acids and the proper cycling of carnitine, directly impacting the steady-state levels of acylcarnitines in the body.[1] Therefore, genetic variations in genes affecting mitochondrial processes can have far-reaching implications for overall metabolic health and specific metabolite levels. [1]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs9924150 rs6497490 rs10163426 | ACSM2A | serum metabolite level X-11478 measurement X-18921 measurement X-21319 measurement indoleacetylglutamine measurement |
| rs12924989 | ACSM5 | indoleacetoylcarnitine measurement |
| rs369344065 | ACSM2A - ACSM2B | indoleacetoylcarnitine measurement |
| rs28415528 | THEM4 | serum metabolite level X-18921 measurement 3-hydroxyoctanoate measurement cis-4-decenoate (10:1n6) measurement 3-hydroxydecanoate measurement |
| rs1171617 | SLC16A9 | carnitine measurement urate measurement gout testosterone measurement X-11261 measurement |
| rs111794350 | LYRM1 | indoleacetoylcarnitine measurement |
Classification, Definition, and Terminology
Section titled “Classification, Definition, and Terminology”Chemical Identity and Physiological Role
Section titled “Chemical Identity and Physiological Role”Indoleacetoylcarnitine is identified as a member of the acylcarnitine family, a group of metabolic compounds formed through the esterification of fatty acids with carnitine. This chemical structure is critical to its biological function, as acylcarnitines primarily serve as carriers for fatty acids, facilitating their transport across the mitochondrial membrane. Within the mitochondria, these fatty acids undergo beta-oxidation, a key process for generating cellular energy.[1] The presence and levels of various acylcarnitines in biological systems are therefore direct indicators of the efficiency and balance of fatty acid metabolism.
Metabolic Classification and Genetic Associations
Section titled “Metabolic Classification and Genetic Associations”Acylcarnitines are broadly classified based on the chain length of the fatty acid they carry, distinguishing categories such as short-chain, medium-chain, and long-chain acylcarnitines. [1] This classification is metabolically significant because different chain lengths are processed by specific enzymes. For instance, genetic variations in the SCAD (short-chain acyl-Coenzyme A dehydrogenase) gene, such as rs2014355 , have shown strong associations with ratios of short-chain acylcarnitines (e.g., C3 and C4), while variants in the MCAD (medium-chain acyl-Coenzyme A dehydrogenase) gene, like rs11161510 , are linked to medium-chain acylcarnitine levels. [1] These genetic-metabolite associations underscore the role of specific enzymes in maintaining distinct acylcarnitine profiles, which can reflect underlying metabolic health or predispositions.
Measurement and Biomarker Utility
Section titled “Measurement and Biomarker Utility”The operational definition and measurement of acylcarnitines, including specific compounds like indoleacetoylcarnitine, are primarily established through advanced analytical techniques such as targeted quantitative metabolomics. Electrospray ionization tandem mass spectrometry (ESI-MS/MS) is a standard platform utilized to determine the fasting serum concentrations of a wide array of endogenous metabolites, including a panel of up to 29 different acylcarnitines.[1] Such precise measurement allows for the use of acylcarnitine concentrations and their ratios as valuable biomarkers. The conceptual framework recognizes that the ratio of two metabolites, representing an enzymatic substrate and product, can approximate the activity of the enzyme responsible for their interconversion [1] thereby providing insight into metabolic pathway functionality and potential diagnostic utility.
Biological Background
Section titled “Biological Background”(No information about indoleacetoylcarnitine is available in the provided context.)
Metabolic Pathways of Acylcarnitine Species
Section titled “Metabolic Pathways of Acylcarnitine Species”Acylcarnitines, encompassing various chain lengths and structures, including species like indoleacetoylcarnitine, are central to lipid metabolism, primarily by facilitating the transport and utilization of fatty acids. These molecules are essential intermediaries that enable fatty acids to be transported into the mitochondria, where they undergo beta-oxidation to generate energy for cellular functions.[1] Key enzymes such as short-chain acyl-Coenzyme A dehydrogenase (SCAD) and medium-chain acyl-Coenzyme A dehydrogenase (MCAD) initiate this catabolic process, demonstrating specific preferences for the chain lengths of the fatty acids they process. [1]The formation of acylcarnitines, through the binding of fatty acids to free carnitine, represents a critical step in controlling the metabolic flux of lipids destined for energy production.
Genetic and Regulatory Mechanisms in Acylcarnitine Metabolism
Section titled “Genetic and Regulatory Mechanisms in Acylcarnitine Metabolism”The intricate metabolism of acylcarnitines is significantly influenced by genetic factors that regulate the function of enzymes involved in fatty acid oxidation. Specific genetic variants, such as intronic single nucleotide polymorphisms (SNPs) likers2014355 within the SCAD gene and rs11161510 in the MCAD gene, have been strongly correlated with variations in the ratios of short-chain acylcarnitines (C3 and C4) and medium-chain acylcarnitines, respectively, in human serum. [1]These associations highlight a regulatory mechanism where genetic polymorphisms can impact enzyme efficiency or expression, thereby modulating the rates at which fatty acids are processed. Although the precise molecular mechanisms by which these intronic SNPs exert their regulatory effects are not explicitly detailed, their robust association with metabolite profiles underscores their importance in metabolic regulation and overall flux control.[1]
Systems-Level Integration of Lipid Pathways
Section titled “Systems-Level Integration of Lipid Pathways”Acylcarnitine metabolism is not an isolated process but rather an integral part of a complex, interconnected system of lipid pathways, characterized by extensive crosstalk and network interactions. As an acylcarnitine, indoleacetoylcarnitine participates in this metabolic network, linking the availability of fatty acids in the cytoplasm with their oxidative consumption in the mitochondria.[1] This systems-level integration ensures a coordinated cellular response to varying energy demands, allowing for dynamic adjustments in lipid storage and utilization. The observed genetic influences, such as the associations between SCAD and MCAD variants and specific acylcarnitine ratios, illustrate a hierarchical regulatory control where genetic predispositions translate into measurable changes in circulating metabolites, representing emergent properties of the integrated metabolic system. [1]
Disease-Relevant Mechanisms in Acylcarnitine Metabolism
Section titled “Disease-Relevant Mechanisms in Acylcarnitine Metabolism”Dysregulation within the acylcarnitine metabolic pathways carries substantial disease relevance, particularly concerning inherited metabolic disorders and conditions affecting lipid homeostasis. Perturbations in the activity of enzymes likeSCAD and MCAD, often linked to genetic variants, can lead to abnormal acylcarnitine profiles that serve as biomarkers for metabolic dysfunction. [1] While the provided studies focus on identifying genetic associations with metabolite levels, the broader implications are that such pathway dysregulation can contribute to various pathophysiological states. A deeper understanding of these specific metabolic signatures and their underlying genetic causes may facilitate the identification of novel therapeutic targets and aid in the development of strategies to mitigate the impact of metabolic diseases. [1]
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, p. e1000282.
[2] Yuan, X., et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, 2008.
[3] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, 2008.
[4] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, 2008.
[5] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007.
[6] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2009.
[7] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.