Indolelactate
Introduction
Section titled “Introduction”Indolelactate is an organic acid and a metabolite primarily derived from the amino acid tryptophan. It is part of the broader metabolic landscape, and its levels can be influenced by various biological processes, including gut microbial activity and host metabolism. Research in metabolomics and genome-wide association studies (GWAS) aims to identify how genetic variations influence the homeostasis of key metabolites like indolelactate, providing insights into human physiology and disease.[1]
Biological Basis
Section titled “Biological Basis”Indolelactate, as an organic anion, is implicated in metabolic pathways. Studies suggest that the metabolism of glucose, facilitated by transporters such asGLUT9 (also known as SLC2A9), can alter levels of lactate and other organic anions, leading to consequent changes in organic acid profiles. [2] GLUT9is a facilitative glucose transporter protein[2], [3]and plays a significant role in the transport of various substances. The interplay between glucose metabolism and organic acid levels is a fundamental aspect of cellular energy regulation.
Clinical Relevance
Section titled “Clinical Relevance”The levels of metabolites like indolelactate are relevant in the context of broader metabolic health. Variations in genes associated with metabolic traits, such as those influencing glucose and uric acid levels, are actively investigated. For instance,MTNR1Bvariants have been associated with glucose levels and are thought to mediate melatonin’s inhibitory effect on insulin secretion.[4] PANK1, encoding pantothenate kinase, is critical for coenzyme A synthesis, and its disruption can lead to a hypoglycemic phenotype.[4] Furthermore, SLC2A9 (GLUT9) is strongly associated with serum uric acid concentrations, with pronounced sex-specific effects[2], [3]. [5]Altered organic anion metabolism, potentially involving compounds like indolelactate, could contribute to the pathogenesis of conditions linked to these metabolic pathways, such as hyperuricemia and gout[2]. [5]
Social Importance
Section titled “Social Importance”Understanding the genetic and environmental factors that influence metabolite profiles, including indolelactate, holds significant social importance. Metabolic traits like glucose, insulin resistance, and uric acid levels are key biomarkers for widespread chronic diseases such as type 2 diabetes, cardiovascular disease, and gout[4], [5], [6]. [1] By identifying genetic variants that impact these metabolic pathways, research can contribute to improved risk prediction, prevention strategies, and personalized therapeutic approaches for these conditions, ultimately enhancing public health.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Studies investigating genetic associations are often constrained by sample size, which can limit the power to detect genetic effects that explain a small proportion of phenotypic variation, such as less than 4%.[7] This means that numerous variants with subtle, yet biologically significant, influences may remain undiscovered. Furthermore, the reliance on imputation to expand genomic coverage, while beneficial, introduces potential inaccuracies, with reported error rates ranging from 1.46% to 2.14% per allele in some studies. [8] The quality of imputation can vary, with some imputed SNPs having very low confidence, which necessitates stringent quality control thresholds (e.g., R-squared > 0.3 or posterior probability > 0.90) that may inadvertently exclude valid associations. [9]
Statistical rigor also presents challenges, including the need to perform extensive transformations for non-normally distributed phenotypes and adjust for numerous covariates such as age, sex, and various health conditions. [10]While these adjustments are crucial for minimizing confounding, the complexity can impact the interpretability of results. The initial discovery phases of genome-wide association studies (GWAS) can also be susceptible to effect-size inflation, where the reported effects are larger than true effects, particularly for less robust signals.[8] This, coupled with the inherent difficulties in replication, where differences in study design, power, or the specific genetic variants assayed across studies can lead to non-replication at the SNP level, suggests that some associations may be false positives or represent proxy signals for different causal variants within the same gene. [4]
Generalizability and Phenotype Definition
Section titled “Generalizability and Phenotype Definition”A significant limitation in many genetic association studies is the restricted generalizability of findings, primarily due to the composition of study cohorts. Many investigations predominantly include individuals of European ancestry, with explicit exclusion of non-European populations in some cases. [11] This narrow demographic focus means that genetic associations discovered may not be directly transferable or have the same effect sizes in more diverse populations, limiting the broader applicability of the research. Additionally, some studies employ specific recruitment strategies, such as cohorts enriched for certain diseases or nested case-control designs, which can introduce cohort biases compared to purely population-based samples. [12]
The precise definition and measurement of phenotypes also pose limitations. Variability in assay methodologies and demographic differences across populations can lead to discrepancies in mean phenotype levels, necessitating study-specific quality control and analytical criteria. [12] To ensure data quality, studies often implement strict exclusion criteria, such as removing individuals who have not fasted, are diabetic, or are on specific medications. [10]While important for reducing noise, these exclusions can reduce sample size and limit the generalizability of findings to the broader population, particularly to individuals with co-morbidities or those undergoing treatment. Furthermore, genetic effects might be sex-specific, yet many analyses are pooled across sexes to manage the multiple testing burden, potentially overlooking important sex-differential genetic influences.[13]
Environmental Confounders and Remaining Knowledge Gaps
Section titled “Environmental Confounders and Remaining Knowledge Gaps”Genetic associations do not exist in isolation, and a major limitation of many studies is the incomplete accounting for environmental or gene–environment (GxE) interactions. Genetic variants can influence phenotypes in a context-specific manner, with their effects modulated by various environmental factors, such as dietary intake. [7] However, comprehensive investigations into these complex GxE interactions are often not undertaken, leading to an incomplete understanding of the full genetic architecture of a trait. [7]While studies attempt to adjust for known confounders like age, sex, body mass index, smoking status, and various disease states, the multifactorial nature of most traits means that unmeasured or unknown environmental influences can still obscure or modify genetic effects.[14]
Despite advances in genomic coverage, current genotyping arrays and imputation panels may still not capture all genetic variation, particularly less common or rare variants, leading to an incomplete picture of the genetic landscape. [7] This partial coverage contributes to the phenomenon of “missing heritability,” where identified genetic variants explain only a fraction of the observed phenotypic variation. Moreover, GWAS primarily identify statistical associations between common genetic markers and traits, which are often not the causal variants themselves. Understanding the comprehensive role of a candidate gene or the precise biological mechanisms underlying an association often requires further in-depth functional studies that extend beyond the scope of initial GWAS, highlighting a significant remaining knowledge gap. [13]
Variants
Section titled “Variants”Variants across several genes demonstrate diverse roles in metabolic pathways, with implications for the regulation of tryptophan metabolism and, consequently, the production of indolelactate. Key enzymes in the kynurenine pathway, such asTDO2 and KYAT1, directly influence the fate of tryptophan, a precursor for indolelactate. The variantrs200135226 in TDO2 is particularly relevant, as TDO2(Tryptophan 2,3-Dioxygenase) initiates the kynurenine pathway, which consumes tryptophan and thus competes with pathways leading to indolelactate. Similarly, variants likers199627869 , rs76253609 , and rs1029625062 in KYAT1(Kynurenine Aminotransferase 1) affect a downstream step in this pathway, influencing the balance of tryptophan metabolites. TheWARS2-AS1 gene, with variant rs72994370 , is an antisense RNA for WARS2, which is involved in charging tRNA with tryptophan; its regulation could impact overall tryptophan availability for various metabolic routes, including the synthesis of indolelactate. These genetic variations contribute to the complex interplay of metabolic processes that can influence circulating levels of indolelactate and related compounds.[4], [15]Other variants influence broader aspects of cellular metabolism and transport. The SLC17A1 gene, associated with rs9461218 , belongs to a family of solute carriers responsible for transporting various substances across cell membranes, including organic anions and phosphates, which are fundamental to nutrient uptake and waste removal. While its direct link to indolelactate is indirect, efficient cellular transport is crucial for maintaining metabolic homeostasis. TheGOT2 gene, associated with rs11647808 , encodes mitochondrial aspartate aminotransferase, an enzyme critical for amino acid metabolism and the malate-aspartate shuttle, which are essential for cellular energy production. Variants affectingGOT2activity have been significantly associated with liver function markers like alanine aminotransferase (ALT) and aspartate aminotransferase (AST), highlighting its central role in liver metabolism, a key site for processing various metabolites, including indoles.[12], [16]The remaining variants are found in or near genes with diverse functions, including non-coding RNAs and structural proteins, which can indirectly influence metabolic health. Variants rs11643460 in the GEMIN8P2 - RPL12P36 region, rs66609725 in RNU6-1155P - RN7SL143P, and rs113759232 in LINC02499are located in non-coding regions or pseudogenes. While their precise mechanisms related to indolelactate are not fully elucidated, non-coding RNAs and pseudogenes can play regulatory roles in gene expression, impacting the production of proteins involved in metabolic pathways. Additionally, variantsrs10819444 , rs34589897 , and rs71497415 are associated with the TBC1D13 - ENDOG region; TBC1D13 is involved in vesicle trafficking, and ENDOG is a mitochondrial nuclease crucial for apoptosis and mitochondrial function, both of which are foundational to cellular health and metabolic regulation. Lastly, rs118186951 in ODF2 (Outer Dense Fiber Of Spermatozoa 2), a gene primarily known for its role in structural components, may also have broader, less direct influences on cell function that could subtly impact metabolic processes. [8], [14]## Biological Background
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs199627869 rs76253609 rs1029625062 | KYAT1, KYAT1 | indolelactate measurement metabolite measurement |
| rs11643460 | GEMIN8P2 - RPL12P36 | indolelactate measurement imidazole lactate measurement |
| rs9461218 | SLC17A1 | guilt measurement etiocholanolone glucuronide measurement O-methylcatechol sulfate measurement 3-methyl catechol sulfate (1) measurement metabolite measurement |
| rs10819444 rs34589897 rs71497415 | TBC1D13 - ENDOG | indolelactate measurement metabolite measurement phenylpyruvate measurement |
| rs66609725 | RNU6-1155P - RN7SL143P | X-11334 measurement imidazole lactate measurement indolelactate measurement Phenyllactate (PLA) measurement |
| rs118186951 | ODF2 | indolelactate measurement |
| rs11647808 | GOT2 - RNU6-1155P | indolelactate measurement 3-formylindole measurement |
| rs113759232 | LINC02499 | low density lipoprotein cholesterol measurement total cholesterol measurement tryptophan measurement 3-methyl catechol sulfate (1) measurement indolelactate measurement |
| rs200135226 | TDO2 | indolelactate measurement |
| rs72994370 | WARS2-AS1 | metabolite measurement indolelactate measurement |
Metabolic Crossroads: Glucose, Lipids, and Urate Homeostasis
Section titled “Metabolic Crossroads: Glucose, Lipids, and Urate Homeostasis”The human body maintains a delicate balance of various metabolic processes, critical for energy production, cellular structure, and waste elimination. Lipid metabolism, for instance, involves the synthesis and breakdown of fats, cholesterol, and lipoproteins. Key enzymes such as 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) are central to cholesterol biosynthesis via the mevalonate pathway, and genetic variations in HMGCR can influence this process by affecting alternative splicing. [17] Similarly, apolipoproteins like APOC3 play a vital role in the structure and metabolism of plasma lipoproteins, with null mutations leading to favorable lipid profiles and potential cardioprotection. [18] Other proteins such as ANGPTL3 and ANGPTL4also regulate lipid levels, impacting triglycerides and high-density lipoprotein (HDL) concentrations, highlighting the intricate network governing lipid homeostasis.[8]
Glucose metabolism is equally fundamental, providing the primary energy source for cells. Genes likeG6PC2 and ABCB1are associated with circulating glucose levels, underscoring their involvement in glucose regulation.[4]The enzyme pantothenate kinase, encoded byPANK1, is crucial for the synthesis of coenzyme A, a molecule vital for numerous metabolic reactions, including those in glucose and lipid pathways; its inhibition can lead to hypoglycemia.[4] Furthermore, the melatonin receptor MTNR1Bis known to modulate insulin secretion, thereby influencing glucose utilization and blood sugar control.[4] These interconnected pathways ensure stable energy supply and proper nutrient utilization, with disruptions contributing to metabolic disorders.
Genetic Architecture of Metabolic Traits
Section titled “Genetic Architecture of Metabolic Traits”Genetic variations profoundly influence an individual’s metabolic profile and susceptibility to disease. Genome-wide association studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) that correlate with metabolic traits such as lipid concentrations, glucose levels, and uric acid dynamics.[1] For example, specific SNPs within the SLC2A9 gene, also known as GLUT9, are strongly associated with serum uric acid levels, influencing both its concentration and renal excretion.[1] A common nonsynonymous variant, Val253Ile, in GLUT9can alter the protein’s structure and function, thereby affecting uric acid homeostasis.[19]
Beyond single genes, genetic clusters also play a role; for instance, variants in the FADS1-FADS2 gene cluster are linked to the composition of fatty acids in phospholipids, demonstrating how genetic factors shape lipid profiles. [1] The impact of DNA variation extends to protein levels, with identified protein quantitative trait loci (pQTLs) for various proteins like IL6R, CCL4, IL18, LPA, GGT1, SHBG, CRP, and IL1RN. [14] These genetic influences can alter protein transcription, cleavage rates, or secretion, providing mechanisms through which genetic predispositions translate into altered protein function and metabolic outcomes. [14]Such genetic insights are crucial for understanding individual differences in metabolic health and disease risk.
Cellular Transport and Renal Function
Section titled “Cellular Transport and Renal Function”Specialized transporter proteins are essential for moving metabolites across cellular membranes, playing a critical role in tissue-specific metabolism and overall homeostasis. The SLC2A9gene, which encodes the glucose transporter-like protein 9 (GLUT9), is a prime example, functioning as a facilitative glucose transporter.[19] GLUT9 is highly expressed in metabolically active organs such as the liver and in specific kidney segments, including the distal tubules. [2] In the kidney, GLUT9is critical for renal uric acid regulation and clearance, with itsGLUT9ΔN splice variant exclusively found in kidney and placenta, highlighting its tissue-specific roles. [19]
The activity of GLUT9in the kidney can have broader metabolic implications. In distal nephron segments, which are often in a relatively anaerobic state, the metabolism of glucose supplied byGLUT9 can alter the local concentrations of lactate and other organic anions. [2] These changes in anion levels can, in turn, influence the transport of other organic molecules, thereby affecting renal excretion and overall fluid and electrolyte balance. [2] The precise cellular localization and functional characteristics of GLUT9, including how its alternative splicing affects protein trafficking, underscore its complex role in maintaining metabolic equilibrium, particularly for urate and potentially for other organic anions like lactate.[19]
Systemic Implications for Health and Disease
Section titled “Systemic Implications for Health and Disease”Disruptions in metabolic pathways and transport mechanisms have far-reaching consequences for systemic health, contributing to a spectrum of pathophysiological conditions. Altered lipid concentrations, often influenced by genetic variants in genes like HMGCR, APOC3, and ANGPTL3/4, are directly linked to an increased risk of coronary artery disease (CAD).[10]Similarly, dysregulation of glucose metabolism, as seen with variants inG6PC2 or MTNR1B, can predispose individuals to conditions like type 2 diabetes. [4]
Urate homeostasis is another critical aspect of metabolic health, with imbalances leading to significant clinical manifestations. Hyperuricemia, characterized by elevated serum uric acid levels, is influenced bySLC2A9 (GLUT9) variants and is strongly implicated in conditions such as gout, kidney stones, and the broader metabolic syndrome.[2] For instance, the upregulation of GLUT9in diabetic rats suggests a potential link between the metabolic syndrome and hyperuricemia.[19]Furthermore, hereditary fructosemia, a condition caused by aldolase deficiency, is associated with both hypoglycemia and hyperuricemia, illustrating the intricate interconnections between carbohydrate and urate metabolism and their systemic impact on various organ systems.[19]
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] Li, S. et al. “The GLUT9gene is associated with serum uric acid levels in Sardinia and Chianti cohorts.”PLoS Genet, 2007.
[3] Doring, Angela, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430-436.
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[5] Wallace, Cathryn, et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, vol. 82, no. 1, 2008, pp. 139-149.
[6] Chambers, John C., et al. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nat Genet, vol. 40, no. 6, 2008, pp. 716-718.
[7] 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, suppl. 1, 2007, S2.
[8] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 1, 2008, pp. 161–169.
[9] Dehghan, A., 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. 1953–1961.
[10] Kathiresan, S. et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008.
[11] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 40, no. 1, 2008, pp. 129–137.
[12] 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. 520–528.
[13] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, S11.
[14] Melzer, D. et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008.
[15] Saxena, R. et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, 2007.
[16] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S11.
[17] 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, 2008.
[18] Pollin, T.I. et al. “A null mutation in human APOC3 confers a favorable plasma lipid profile and apparent cardioprotection.” Science, 2008.
[19] McArdle, P.F. et al. “Association of a common nonsynonymous variant in GLUT9with serum uric acid levels in old order amish.”Arthritis Rheum, 2008.