Pantothenate

Introduction

Pantothenate, commonly known as Vitamin B5, is an essential water-soluble vitamin indispensable for numerous metabolic processes within the human body. Its name, derived from the Greek word “pantos” meaning “from everywhere,” reflects its widespread presence in a variety of food sources.

Biological Basis

The primary biological role of pantothenate lies in its function as a precursor for the synthesis of Coenzyme A (CoA). CoA is a pivotal coenzyme involved in a vast array of biochemical reactions, central to the metabolism of carbohydrates, fats, and proteins. It plays key roles in energy production, fatty acid synthesis and oxidation, and the synthesis of cholesterol, steroid hormones, and various neurotransmitters. Genetic studies have highlighted the importance of pantothenate metabolism, with mouse chemical knockout studies of pantothenate kinase, encoded by the_PANK1_gene, demonstrating a hypoglycemic phenotype. This provides functional evidence supporting the gene’s role in glucose metabolism.^[1]

Clinical Relevance

Due to its integral role in CoA synthesis, pantothenate is crucial for maintaining overall physiological health. While severe pantothenate deficiency is rare in humans due to its ubiquitous presence in foods, potential symptoms can include fatigue, irritability, and neurological disturbances. The observed hypoglycemic phenotype associated with_PANK1_knockout studies suggests potential clinical implications related to blood glucose regulation, thereby underscoring its relevance in metabolic health.^[1]

Social Importance

Pantothenate is widely distributed in various foods such as meats, vegetables, whole grains, legumes, eggs, and milk, making dietary deficiency uncommon in populations with adequate nutrition. Its broad availability and critical metabolic functions contribute significantly to its general importance in public health, supporting proper cellular function and energy production across diverse populations.

Limitations

Methodological Constraints and Replication Challenges

Despite significant advancements, studies investigating the genetic basis of the trait face inherent methodological and statistical limitations. Many studies operate with moderate cohort sizes, which can lead to insufficient statistical power to detect genetic associations with modest effect sizes, potentially resulting in false negative findings. ^[2] Furthermore, incomplete coverage of genomic variation by genotyping arrays, even with imputation based on reference panels like HapMap, means that some causal variants or genes may be missed due to lack of direct assessment or insufficient linkage disequilibrium with genotyped markers. ^[3] This partial coverage can hinder a comprehensive understanding of candidate genes and their influence on the trait.

A persistent challenge lies in the replication of findings, with some studies noting that only a fraction of previously reported associations are consistently replicated across different cohorts. ^[2] Non-replication can stem from various factors, including initial false positive findings, differences in study populations or methodologies, or insufficient power in replication cohorts. ^[2] Additionally, even within the same gene region, different studies might identify associations with distinct rsIDs, suggesting either multiple causal variants or variations in linkage disequilibrium patterns across populations. ^[4] The ultimate validation of genetic associations often requires independent replication in diverse cohorts and subsequent functional validation to confirm their biological relevance. ^[2]

Generalizability and Phenotypic Heterogeneity

The generalizability of findings is often limited by the demographic characteristics of the study populations, which are frequently composed primarily of individuals of white European ancestry and specific age ranges, such as middle-aged to elderly adults. ^[2] This demographic homogeneity restricts the extent to which findings can be extrapolated to younger individuals or populations of different ethnic or racial backgrounds, where genetic architectures or gene-environment interactions might differ significantly. ^[2] Although efforts are made to mitigate population stratification through methods like genomic control and principal component analysis, residual substructure within seemingly homogeneous groups could still subtly influence association results. ^[5]

Phenotypic assessment also presents challenges, as many traits exhibit complex distributions that necessitate specific statistical transformations to approximate normality, such as log or Box-Cox transformations. ^[6] Such data handling, while necessary, highlights the inherent variability and potential heterogeneity in trait measurements. Moreover, analyses are often performed in a sex-pooled manner to avoid compounding the multiple testing problem, which may inadvertently mask sex-specific genetic associations that only manifest in males or females. ^[3] The use of averaged phenotypic observations, whether from repeated measurements on individuals or from monozygotic twin pairs, aims to improve measurement reliability but also averages out potential within-individual variability. ^[7]

Environmental Confounding and Remaining Knowledge Gaps

Current research often acknowledges, but does not extensively investigate, the role of gene-environment interactions in modulating genetic effects on the trait. Genetic variants can influence phenotypes in a context-specific manner, with environmental factors like diet or lifestyle significantly altering their penetrance or impact.^[8] The absence of comprehensive analyses of these interactions means that the full spectrum of genetic influences on the trait may not be completely understood, and identified associations might be conditional on specific environmental contexts, limiting their broader interpretation. ^[8]

Despite the identification of numerous genetic loci, a substantial portion of the heritability for many complex traits remains unexplained, pointing to remaining knowledge gaps. ^[9] This “missing heritability” could be attributed to several factors, including the cumulative effect of many common variants with very small individual effect sizes, rarer variants not captured by current genotyping platforms, or complex epistatic interactions that are difficult to detect with current methodologies. Continued research with larger sample sizes and improved statistical power, along with advanced genomic sequencing technologies, will be crucial for uncovering these hidden genetic components and further elucidating the polygenic architecture of the trait. ^[9]

Variants

Genetic variations play a crucial role in influencing an individual’s metabolism and nutrient utilization, including that of pantothenate (vitamin B5). Pantothenate is an essential precursor for Coenzyme A (CoA), a vital molecule involved in numerous metabolic pathways, such as energy production, fatty acid synthesis and oxidation, and cholesterol synthesis. Variants within genes directly involved in pantothenate transport, as well as those regulating broader metabolic processes, can impact its availability and function within the body.^[2]

Variations in the SLC5A6 gene, such as rs34303460 , rs78250434 , and rs1395 , are particularly relevant as SLC5A6encodes the Sodium-dependent Multivitamin Transporter (SMVT). This transporter is responsible for the cellular uptake of pantothenate, biotin, and lipoate, making it a critical gatekeeper for pantothenate supply to cells throughout the body. Alterations inSLC5A6can affect the efficiency of pantothenate absorption and distribution, potentially influencing cellular CoA levels and the activity of CoA-dependent enzymes. Such variations may therefore imp

Other solute carrier genes also contribute to the intricate balance of cellular metabolites, indirectly affecting pantothenate-related pathways. For instance,rs35875210 in SLC17A1, which encodes a sodium-phosphate cotransporter, can influence renal handling of phosphate and other organic anions, thereby modulating systemic metabolic homeostasis. Similarly,rs1561535 near SLC30A3 (Zinc Transporter 3, ZnT3) affects zinc transport, a micronutrient essential for the activity of numerous enzymes, some of which interact with pantothenate-dependent pathways. Variants likers35489850 in the SLC16A11-CLEC10A region and rs1171616 in SLC16A9relate to monocarboxylate transporters, which facilitate the movement of crucial metabolic intermediates such as lactate and pyruvate. Efficient transport of these molecules is vital for energy production, a process where pantothenate, as part of CoA, plays a central role.^[2]

Beyond transporters, genes involved in broad cellular regulation and lipid metabolism also present variants that can have downstream effects on pantothenate. The geneCAD, a trifunctional enzyme crucial for de novo pyrimidine synthesis, is involved in fundamental cellular building blocks, and its efficiency can broadly impact cell growth and metabolism. Variants such as rs188969860 in the TPT1P9-LINC02578 region, and rs536922358 in CNOT6L (a component of the CCR4-NOT mRNA deadenylation complex), influence gene expression and RNA stability, which can alter the abundance of proteins in various metabolic pathways. Furthermore, rs200931857 in SETBP1, a transcriptional regulator, can affect the global gene expression landscape. Lastly, rs149087233 in ABCA1, a key regulator of cholesterol efflux and HDL formation, directly impacts lipid metabolism, a pathway intimately linked with pantothenate through its role in CoA synthesis and breakdown of fatty acids and cholesterol.^[9]

Key Variants

RS ID	Gene	Related Traits
rs34303460 rs78250434	TCF23 - SLC5A6	pantothenate measurement
rs1395	SLC5A6	blood glucose amount pantothenate measurement polyunsaturated fatty acid measurement fatty acid amount linoleic acid measurement
rs35875210	SLC17A1	pantothenate measurement phenylalanine measurement
rs1561535	CAD - SLC30A3	lysophosphatidylcholine 14:0 measurement pantothenate measurement
rs35489850	SLC16A11 - CLEC10A	pantothenate measurement diabetes mellitus
rs1171616	SLC16A9	serum metabolite level urate measurement acetylcarnitine measurement N-methylproline measurement propionylcarnitine measurement
rs188969860	TPT1P9 - LINC02578	pantothenate measurement
rs536922358	CNOT6L	pantothenate measurement
rs200931857	SETBP1	pantothenate measurement
rs149087233	ABCA1	pantothenate measurement

Classification, Definition, and Terminology

Defining Pantothenate and its Kinase

Pantothenate, often referred to in the context of its enzymatic derivatives, plays a role in fundamental biological processes. While pantothenate itself is a precursor, its metabolic function is largely mediated by pantothenate kinase, an enzyme critical for various biochemical pathways. Mouse chemical knockout studies have provided functional evidence for the significant involvement of pantothenate kinase in glucose metabolism. This enzyme’s activity is therefore crucial for maintaining metabolic balance within the body.^[1]

Genetic Basis and Associated Terminology

The gene responsible for encoding pantothenate kinase isPANK1. This genetic locus is a key determinant of the enzyme’s presence and activity, thereby influencing the metabolic traits it regulates. Research indicates that PANK1contributes to the overall variability observed in certain metabolic pathways, specifically explaining 0.56% of the total variability in relevant traits. The term “pantothenate kinase” refers to the functional enzyme, whilePANK1 represents its genetic blueprint, linking specific genetic variations to observable biological effects. ^[1]

Metabolic Significance and Phenotypic Impact

The functional importance of pantothenate kinase is prominently highlighted by its impact on glucose metabolism. As demonstrated through mouse chemical knockout studies, a deficiency or inactivation of this enzyme leads to a distinct hypoglycemic phenotype. Hypoglycemia is a clinical classification characterized by abnormally low blood glucose levels, signifying a critical disruption in glucose homeostasis. This finding underscores the enzyme’s essential regulatory role in preventing metabolic dysregulation and maintaining healthy blood sugar levels.^[1]

Causes of Pantothenate Levels

Genetic Predisposition

Pantothenate levels are influenced by specific genetic factors, with thePANK1 gene playing a significant role. PANK1encodes pantothenate kinase, a crucial enzyme involved in the synthesis of coenzyme A, which is vital for numerous metabolic processes.^[1] Research indicates that variants within PANK1 can contribute to the variability of related metabolic traits. For instance, a locus on chromosome 10 at rs11185790 , located in an intron of PANK1, has been associated with insulin levels.^[1]Mouse knockout studies of pantothenate kinase have also demonstrated a hypoglycemic phenotype, underscoring the functional importance of this gene in glucose metabolism.^[1] This genetic locus alone accounts for approximately 0.56% of the total variability observed for these metabolic traits. ^[1]

Gene-Environment Interactions

While individual genetic loci, such as those within PANK1, explain a portion of the variability in metabolic traits, the full spectrum of causal factors often involves complex interactions between an individual’s genetic makeup and their environment. ^[1]Studies on metabolic traits suggest that additional genetic influences may be identified by analyzing how the effect of a single nucleotide polymorphism (SNP) on a trait depends on various environmental covariates.^[1]These gene-environment interactions highlight that environmental triggers can modulate the expression or impact of genetic predispositions, leading to a more nuanced understanding of pantothenate’s regulation.

Pharmacological and Metabolic Influences

Beyond direct genetic effects, pantothenate levels and related metabolic pathways can be influenced by pharmacological agents and broader metabolic states. The enzyme pantothenate kinase, encoded byPANK1, is known to be induced by bezafibrate, a hypolipidemic agent. ^[1]This suggests that certain medications can directly impact the synthesis of coenzyme A and, by extension, pantothenate metabolism. Furthermore, the observed hypoglycemic phenotype in mouse models with pantothenate kinase knockouts points to a critical role of pantothenate in maintaining glucose homeostasis, indicating a metabolic comorbidity where dysregulation could affect pantothenate levels.^[1]

Biological Background

Molecular and Enzymatic Role of Pantothenate Kinase

Pantothenate kinase, an enzyme encoded by thePANK1gene, serves as a critical biomolecule within cellular metabolism. This enzyme plays a specific role in glucose metabolism, where its catalytic activity is essential. The proper functioning of pantothenate kinase is therefore fundamental for the progression of metabolic processes related to glucose. Its presence and activity are integral to maintaining metabolic equilibrium at a molecular level..^[4]

Involvement in Glucose Metabolism Pathways

The primary biological mechanism associated with pantothenate kinase is its direct or indirect involvement in glucose metabolism. This enzyme participates in the complex network of molecular pathways that regulate how cells process and utilize glucose. Functional evidence from mouse chemical knockout studies demonstrates a clear link between pantothenate kinase and the maintenance of glucose levels. These studies highlight the enzyme’s role in the intricate cellular functions that underpin metabolic processes..^[4]

Pathophysiological Consequences of Disruption

Disruptions in the normal function of pantothenate kinase can lead to significant pathophysiological processes, particularly affecting metabolic homeostasis. Chemical knockout studies in mice, where the function of pantothenate kinase was inhibited, resulted in a pronounced hypoglycemic phenotype. This finding indicates a homeostatic disruption where the body struggles to maintain adequate blood glucose levels. Such a metabolic imbalance underscores the enzyme’s importance in preventing conditions like hypoglycemia..^[4]

Genetic Contribution to Metabolic Regulation

The PANK1gene, which codes for pantothenate kinase, represents a genetic factor influencing metabolic traits. Through genetic mechanisms, this gene contributes to the overall variability observed in metabolic profiles within a population. Specifically, thePANK1 gene alone explains 0.56% of the total variability in relevant metabolic traits, demonstrating a quantifiable genetic influence. This highlights how specific gene functions, like those of PANK1, can modulate systemic metabolic consequences.. ^[4]

Pathways and Mechanisms

Coenzyme A Biosynthesis and Central Metabolism

Pantothenate, also known as vitamin B5, is an essential precursor for the biosynthesis of Coenzyme A (CoA), a vital cofactor involved in numerous metabolic reactions throughout the cell. The initial and rate-limiting step in this pathway is catalyzed by pantothenate kinase, an enzyme encoded by thePANK1 gene. ^[1]CoA plays a central role in both catabolic and anabolic processes, including the oxidation and synthesis of fatty acids, and acts as a key acyl group carrier in the tricarboxylic acid cycle, thereby interconnecting carbohydrate, lipid, and protein metabolism.^[1]This foundational function of pantothenate underscores its critical importance in maintaining overall cellular energy homeostasis and the synthesis of macromolecules.

Regulation of Lipid and Glucose Homeostasis

The metabolic pathways governed by pantothenate, particularly through the action of pantothenate kinase, are crucial for regulating both lipid and glucose homeostasis. ThePANK1 enzyme, which is central to CoA synthesis, has been observed to be induced by bezafibrate, a known hypolipidemic agent. ^[1]Furthermore, studies involving chemical knockouts of pantothenate kinase in mice have resulted in a hypoglycemic phenotype, providing direct functional evidence of its role in maintaining glucose balance.^[1]These findings highlight how the efficient conversion of pantothenate into CoA is indispensable for healthy plasma lipid concentrations and the systemic regulation of glucose levels.

Genetic Influence and Pharmacological Modulation

Variations in genes associated with pantothenate metabolism can significantly influence an individual’s metabolic profile, with specific genetic variants in thePANK1gene linked to metabolic traits. For instance, a single nucleotide polymorphism (SNP) located within an intron ofPANK1, rs11185790 , has been associated with levels of insulin, suggesting a genetic impact on glucose metabolism mediated by pantothenate kinase activity.^[1] Beyond intrinsic genetic factors, the pharmacological induction of PANK1by agents like bezafibrate demonstrates a powerful regulatory mechanism through which pantothenate metabolism can be therapeutically modulated, especially in the context of managing dyslipidemia.^[1]This interplay between genetic predispositions and external pharmacological interventions illustrates the complex control mechanisms governing pantothenate-dependent pathways.

Interplay with Insulin Signaling and Systems Integration

The observed association between genetic variants in PANK1and insulin levels, alongside the hypoglycemic phenotype documented in mouse models lacking functional pantothenate kinase, suggests a significant crosstalk between pantothenate metabolism and insulin signaling pathways. This indicates a systems-level integration where the synthesis and availability of CoA, primarily regulated by pantothenate kinase activity, directly influence the body’s sensitivity to insulin and its capacity to regulate blood glucose.^[1]Such intricate interactions are fundamental for understanding the emergent properties of metabolic networks and how dysregulation within the pantothenate utilization pathway can lead to cascading effects across broader endocrine and metabolic functions.

Clinical Relevance

[No information about pantothenate is available in the provided context.]

References

[1] Sabatti C, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, vol. 41, no. 1, 2009, pp. 35-46. PMID: 19060910.

[2] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, vol. 8, suppl. 1, 2007, S11.

[3] Yang, Q., et al. “Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.”BMC Medical Genetics, vol. 8, suppl. 1, 2007, S12.

[4] Sabatti, C. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, PMID: 19060910.

[5] Pare, G., et al. “Novel association of ABO histo-blood group antigen with soluble ICAM-1: results of a genome-wide association study of 6,578 women.” PLoS Genetics, vol. 4, no. 7, 2008, e1000118.

[6] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, e1000072.

[7] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 83, no. 6, 2008, pp. 759–69.

[8] 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 Medical Genetics, vol. 8, suppl. 1, 2007, S2.

[9] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nature Genetics, vol. 41, no. 1, 2009, pp. 56–65.