Correolide

Correolide is a natural product classified as a sesquiterpene lactone, a large and diverse group of organic compounds predominantly found in plants. These compounds are secondary metabolites known for their complex chemical structures and a wide array of biological activities. Correolide, like many other sesquiterpene lactones, is often isolated from various plant species, particularly those within theCompositae (daisy) family. Its presence in plants suggests potential roles in defense mechanisms against herbivores and pathogens.

Biological Basis

The biological effects of correolide are primarily mediated through its interactions with key cellular signaling pathways. A notable mechanism involves the inhibition of the nuclear factor-kappa B (NF-κB) pathway. NF-κBis a protein complex that controls transcription of DNA, cytokine production, and cell survival, playing a critical role in inflammation and immune responses. By modulatingNF-κBactivity, correolide can influence the expression of genes involved in inflammation, cell proliferation, and programmed cell death (apoptosis). This molecular action underlies many of its observed pharmacological properties.

Clinical Relevance

Correolide has attracted significant scientific interest due to its promising preclinical therapeutic potential, particularly in the fields of anti-inflammatory and anti-cancer research. Its ability to suppress inflammatory pathways suggests it could be a candidate for treating inflammatory conditions. Furthermore, studies have explored its cytotoxic effects against various cancer cell lines, demonstrating its capacity to induce apoptosis and inhibit tumor growth in experimental models. These findings position correolide as a lead compound for further investigation into novel drug development.

Social Importance

The discovery and study of natural products like correolide underscore the immense value of biodiversity as a source of novel therapeutic agents. As a compound exhibiting both anti-inflammatory and anti-cancer properties, correolide contributes to the ongoing search for new pharmaceuticals to address prevalent and challenging diseases. Its exploration highlights the importance of ethnobotany and traditional medicinal practices in guiding modern drug discovery, offering unique chemical structures and mechanisms of action that can inspire the synthesis of new drugs and improve human health.

Limitations

Methodological and Statistical Constraints

Many genome-wide association studies (GWAS) face inherent methodological and statistical limitations that can impact the interpretation of findings. A common challenge is insufficient statistical power, particularly for detecting genetic effects that explain only a modest proportion of phenotypic variation, given the extensive multiple testing involved in scanning the entire genome. ^[1] This can lead to false negative findings, where true associations are missed, or false positive results, despite statistical support, which necessitate replication in independent cohorts for validation. ^[1]Furthermore, the reliance on a subset of single nucleotide polymorphisms (SNPs) from platforms like the Affymetrix 100K chip means that studies may not fully cover all genetic variation, potentially missing associations within genes or regions not adequately genotyped.^[2]

Replication of initial findings often proves difficult, with some meta-analyses showing only about one-third of reported associations successfully replicated. ^[1] This can be due to initial findings being false positives, differences in study cohorts that modify gene-phenotype associations, or inadequate power in replication studies leading to false negatives. ^[1] Additionally, analytical approaches that do not fully account for population stratification, or ignoring relatedness among sampled individuals, can lead to misleading p-values and inflated false-positive rates, although some studies employ methods like genomic control or family-based tests to mitigate these issues. ^[3] The choice to perform only sex-pooled analyses, while reducing the multiple testing burden, may also obscure sex-specific genetic associations with certain phenotypes. ^[4]

Generalizability and Phenotype Assessment

The demographic characteristics of study cohorts significantly influence the generalizability of findings. Many studies are conducted in cohorts that are predominantly of white European descent and often middle-aged to elderly, which limits the applicability of the results to younger individuals or those of other ethnic and racial backgrounds. ^[1] Such cohort biases, including potential survival bias from DNA collection at later examinations, mean that observed genetic associations might not hold true across diverse populations or age groups. ^[1]

Phenotype assessment methods also present limitations, especially when traits are measured repeatedly over extended periods. Averaging echocardiographic traits over twenty years, for example, can introduce misclassification due to evolving diagnostic equipment and may mask age-dependent gene effects by assuming constant genetic and environmental influences across a wide age range. ^[5] This averaging strategy, while intended to reduce regression dilution bias, may inadvertently obscure dynamic biological processes or context-specific genetic influences that vary with age or other factors. ^[5]

Unaccounted Factors and Research Gaps

Many genetic associations are complex and can be modulated by environmental influences, leading to context-specific genetic effects. However, a common limitation in GWAS is the lack of comprehensive investigation into gene-environment interactions. ^[5]For instance, the impact of genetic variants on traits like left ventricular mass has been shown to vary with factors such as dietary salt intake, highlighting the importance of considering environmental confounders.^[5] The omission of such analyses means that the full picture of how genetic variants contribute to a phenotype, particularly in real-world settings, remains incomplete.

Despite identifying numerous genetic loci, a fundamental challenge in GWAS is to move beyond statistical associations to understand their functional implications and prioritize SNPs for follow-up. ^[1] The current findings represent steps toward understanding the genetic architecture of traits, but there remains a substantial gap in knowledge regarding the precise biological mechanisms through which many identified variants exert their effects. ^[1] Acknowledging these remaining knowledge gaps underscores the need for further functional studies and external replication in diverse cohorts to fully validate and translate genetic discoveries. ^[1]

Variants

Genetic variations play a crucial role in individual susceptibility to various metabolic and inflammatory conditions, which correolide, a bioactive compound, may interact with or modulate. Variants in genes likeHMGCR, IL-6, SLC2A9, MLXIPL, and MC4Rhighlight the complex genetic landscape influencing lipid metabolism, inflammation, and energy balance. Understanding these genetic predispositions helps in elucidating potential individual responses to therapeutic or dietary interventions, including natural products like correolide.

Variations in the HMGCRgene, which encodes 3-hydroxy-3-methylglutaryl-CoA reductase, a key enzyme in cholesterol synthesis, are significantly associated with circulating low-density lipoprotein (LDL) cholesterol levels. Common single nucleotide polymorphisms (SNPs) withinHMGCR have been shown to influence LDL-cholesterol concentrations and affect the alternative splicing of exon 13, potentially altering the enzyme’s activity or regulation. ^[6]These genetic differences can lead to varying lipid profiles among individuals, influencing their risk for cardiovascular diseases and potentially affecting how correolide might impact lipid-lowering pathways or overall metabolic health..^[7]

The IL-6gene, encoding the pro-inflammatory cytokine interleukin-6, features a promoter polymorphism,rs1800795 (G(-174)C), which has been extensively studied for its role in inflammation and metabolic disorders. This specific variant is associated with altered plasma concentrations of inflammatory markers and an increased risk of type 2 diabetes and peripheral arterial disease.^[8] Furthermore, the rs1800795 polymorphism has been linked to insulin resistance and influences serumIL-6levels, impacting overall inflammatory responses and mortality in the elderly.^[9]Given correolide’s potential anti-inflammatory properties, understanding an individual’srs1800795 genotype could provide insights into personalized responses to correolide-based interventions aimed at mitigating inflammation or improving insulin sensitivity.

Another significant gene, SLC2A9, which encodes a glucose transporter-like protein, functions as a crucial urate transporter. Variations withinSLC2A9profoundly influence serum urate concentrations, affecting both urate excretion and the risk of developing gout.^[10] The impact of SLC2A9variants on uric acid levels is particularly notable for its pronounced sex-specific effects, suggesting a complex interplay of genetic and biological factors.^[11]Given that correolide may influence various metabolic pathways, its interaction with urate metabolism, potentially mediated throughSLC2A9, could have implications for individuals with hyperuricemia or gout.

Genetic variations in MLXIPL and near MC4R also contribute to metabolic health. The MLXIPLgene, which codes for MondoA, a transcription factor involved in regulating glycolysis and fatty acid synthesis, shows variations associated with plasma triglyceride levels.^[12] Similarly, common genetic variations located near the MC4Rgene, which encodes the melanocortin 4 receptor central to appetite regulation and energy expenditure, are linked to waist circumference and insulin resistance.^[13]These genetic insights into triglyceride metabolism and appetite control are relevant to correolide, as the compound may influence energy balance, fat storage, or inflammatory processes that are often intertwined with these metabolic pathways.

Key Variants

RS ID	Gene	Related Traits
chr13:105814845	N/A	correolide measurement

Biological Background

Genetic Regulation of Lipid and Fatty Acid Metabolism

The intricate balance of lipid and fatty acid metabolism is profoundly influenced by specific genetic mechanisms and regulatory networks, which dictate the synthesis, transport, and breakdown of these crucial biomolecules. For instance, the MLXIPLgene, which encodes for MLX interacting protein like, has been associated with plasma triglyceride levels, a key indicator of lipid metabolism.^[12] Similarly, the HMGCRgene, encoding 3-hydroxy-3-methylglutaryl coenzyme A reductase, is a critical enzyme in the mevalonate pathway, responsible for cholesterol biosynthesis, and common single nucleotide polymorphisms (SNPs) within this gene, particularly those affecting the alternative splicing of exon 13, can significantly impact low-density lipoprotein cholesterol (LDL-C) levels.^[6] Beyond cholesterol, the composition of fatty acids in phospholipids, essential components of cell membranes, is under the genetic influence of the FADS1 FADS2gene cluster, where common genetic variants and their reconstructed haplotypes are associated with the precise polyunsaturated fatty acid profiles.^[14]

These genetic predispositions contribute to the overall metabolic phenotype, impacting the homeostasis of key lipids, carbohydrates, and amino acids. Genome-wide association studies (GWAS) have been instrumental in identifying genetic variants that correlate with changes in serum metabolite profiles, providing deeper insights into potentially affected metabolic pathways.^[15] Such studies reveal how genetic polymorphisms can lead to alterations in lipid side chain composition, like in plasmalogen/plasmenogen phosphatidylcholines, by influencing the number of carbons and double bonds in fatty acid residues. ^[15] Understanding these genetic underpinnings is crucial for elucidating the molecular and cellular pathways that govern lipid metabolism and its systemic consequences.

Urate Homeostasis and Associated Disorders

The maintenance of uric acid levels, a critical aspect of homeostatic regulation, is significantly governed by specific transport proteins and their genetic variants. TheSLC2A9 gene, also known as GLUT9, encodes a glucose transporter protein that has been identified as a primary urate transporter.^[10]This protein plays a pivotal role in influencing serum urate concentration and the excretion of urate, thereby directly impacting the risk and manifestation of gout.^[10] Genetic variations within SLC2A9have been shown to be strongly associated with serum uric acid levels, with notable sex-specific effects on these concentrations.^[11]

A common nonsynonymous variant in GLUT9 (SLC2A9) has been specifically linked to altered serum uric acid levels, highlighting the importance of this gene in purine metabolism and its dysregulation.^[16]Disruptions in this delicate balance, often mediated by these genetic factors, can lead to hyperuricemia, a pathophysiological process characterized by elevated uric acid in the blood, which is a key precursor to the development of gout. Thus, theSLC2A9gene and its associated polymorphisms represent a critical genetic mechanism underlying urate homeostasis and the pathogenesis of urate-related disorders.

Molecular Pathways in Cardiometabolic Disease

Cardiometabolic diseases, including type 2 diabetes and various forms of cardiovascular disease, are complex conditions influenced by a network of molecular and cellular pathways, genetic predispositions, and homeostatic disruptions. Genome-wide association analyses have identified specific genetic loci associated with type 2 diabetes and triglyceride levels, indicating a shared genetic architecture for these conditions.^[17]Insulin resistance, a hallmark of type 2 diabetes, is also genetically influenced, with polymorphisms in genes like the interleukin-6 (IL-6) gene promoter (e.g., C-174G) being associated with altered insulin sensitivity.^[9]

Furthermore, inflammatory processes are intricately linked to cardiometabolic health, with IL-6haplotypes playing a role in inflammation and the risk for cardiovascular disease.^[1] Plasma concentrations of inflammatory markers, influenced by the G(-174)C IL-6polymorphism, are also relevant in patients with type 2 diabetes and peripheral arterial disease.^[8] These interconnected molecular pathways, involving hormones, signaling proteins, and regulatory networks, collectively contribute to the pathophysiological processes that define cardiometabolic diseases, emphasizing the multi-faceted genetic and cellular basis of these prevalent health issues.

Systemic Effects and Tissue-Specific Manifestations

The genetic and molecular mechanisms governing metabolism and disease susceptibility manifest as systemic consequences and tissue-specific effects throughout the body. In the context of lipid metabolism, variations in genes likeHMGCR influence hepatic 3-hydroxy-3-methylglutaryl coenzyme A reductase activity, thereby impacting the liver’s role in cholesterol synthesis and overall plasma lipid profiles. ^[18] The liver’s metabolic function is further indicated by plasma levels of liver enzymes, which can also be influenced by genetic loci. ^[19]

Beyond metabolic organs, the cardiovascular system is a key site for the manifestation of these genetic predispositions. Subclinical atherosclerosis, a precursor to cardiovascular events, has been investigated in genome-wide association studies across major arterial territories, revealing systemic impacts of genetic variants.^[2] Moreover, the production of F cells, a type of red blood cell, is influenced by a quantitative trait locus (QTL) mapping to a gene encoding a zinc-finger protein on chromosome 2p15, demonstrating how genetic factors can also affect specific cellular populations within the blood. ^[20] These examples illustrate how genetic variations, acting through molecular pathways, lead to diverse tissue interactions and systemic health outcomes, from metabolic dysregulation to cellular function and organ-level pathology.

Pathways and Mechanisms

Regulation of Core Metabolic Pathways

The homeostasis of various endogenous metabolites, including lipids, carbohydrates, and amino acids, is tightly regulated through complex metabolic pathways. For instance, lipid metabolism involves genes like FADS1 and FADS2, which are part of a gene cluster associated with the composition of fatty acids in phospholipids. ^[21] Genetic variants within this cluster can influence the biosynthesis and modification of polyunsaturated fatty acids. Similarly, the mevalonate pathway, crucial for cholesterol biosynthesis, is regulated by enzymes such as HMG-CoA reductase, encoded by HMGCR, where common genetic variants can affect LDL-cholesterol levels ^{[6], [16], [18], [22], [23], [24], [25]} This alternative splicing represents a post-transcriptional regulatory mechanism that can generate different protein isoforms from a single gene, each potentially with distinct functions or regulatory properties.

Furthermore, post-translational modifications of proteins, although not explicitly detailed for specific metabolites in the provided context, are integral to fine-tuning enzyme activity and protein function. Allosteric control, where molecules bind to a protein at a site other than the active site to alter its activity, is a common regulatory mechanism in metabolic pathways, ensuring rapid adaptation to changing cellular conditions. The identification of genetic variants that alter the homeostasis of key metabolites underscores the importance of gene regulation in shaping the metabolic landscape and the functional readout of an individual’s physiological state. ^[15]

Inter-Pathway Communication and Systemic Effects

Metabolic pathways do not operate in isolation but are intricately interconnected, forming a complex metabolic network characterized by extensive pathway crosstalk and network interactions. This systems-level integration ensures coordinated responses to physiological demands and environmental cues. For instance, dysregulation in uric acid metabolism has been linked to broader systemic conditions, including the metabolic syndrome and renal disease.^[23] Such connections highlight how alterations in one metabolic pathway can have cascading effects across multiple physiological systems.

The concept of emergent properties arises from these complex interactions, where the behavior of the entire metabolic network is more than the sum of its individual pathways. Genome-wide association studies (GWAS) combined with metabolomics aim to probe this human metabolic network in detail, identifying how genetic variants influence intermediate phenotypes and ultimately contribute to the etiology of complex diseases. ^[15]This hierarchical regulation, from genetic variants to metabolite profiles and then to systemic health outcomes, provides a comprehensive view of biological function.

Clinical Relevance of Pathway Dysregulation

Dysregulation within these metabolic pathways is a significant contributor to various common diseases, making them crucial therapeutic targets. Genetic variants that influence lipid concentrations, such as those impacting LDL-cholesterol and triglycerides, are directly associated with the risk of coronary artery disease^{[7], [15], [22], [23], [24]} Identifying major genetically determined metabotypes allows for a more precise approach to intervention, potentially including individualized medication based on a patient’s unique genetic and metabolic profile.

Clinical Relevance

Genetic Risk Prediction and Personalized Prevention Strategies

Genetic risk profiles derived from genome-wide association studies offer significant potential for predicting dyslipidemia and associated cardiovascular risks. Research indicates that incorporating genetic risk scores can improve the discriminative accuracy for dyslipidemia, enhancing the prediction beyond traditional clinical factors such as age, sex, and body mass index.^[22]This improved accuracy suggests that genetic information may be valuable for the early detection of dyslipidemias and related cardiovascular conditions, thereby enabling more timely and effective preventive strategies.^[22]Such advancements support a move towards personalized medicine, allowing for the identification of high-risk individuals who could benefit from tailored interventions or intensified monitoring to prevent disease onset or progression.

Furthermore, specific genetic risk profiles, particularly those related to total cholesterol (TC), have demonstrated a strong association with clinically significant cardiovascular outcomes. Studies have linked TC genetic risk profiles to clinically defined hypercholesterolemia and key markers of subclinical atherosclerosis, including intima media thickness (IMT) and incident coronary heart disease (CHD).^[22]These findings highlight the prognostic value of genetic scores in assessing long-term cardiovascular health and stratifying individuals based on their inherent predisposition to conditions like hypercholesterolemia and atherosclerosis.^[22]This precision in risk stratification can facilitate more informed treatment selection and personalized management plans aimed at mitigating disease progression and improving patient care.

Diagnostic Utility and Monitoring of Metabolic and Inflammatory Traits

Genetic insights extend to various biomarker traits beyond traditional lipid levels, offering potential for enhanced diagnostic utility and refined monitoring strategies. Genome-wide association studies have identified genetic loci influencing plasma levels of liver enzymes, such as gamma-glutamyl transferase, and inflammatory markers like C-reactive protein (CRP).^[1]These genetic associations suggest that genetic testing could contribute to a more comprehensive diagnostic assessment, potentially indicating a predisposition to altered liver function or chronic inflammation, both of which are common in metabolic and cardiovascular disorders.^[1] The identification of cis-acting regulatory variants that influence protein levels, such as the CRP gene’s effect on CRP concentration, further underscores the direct genetic control over these critical biomarkers. ^[1]

The understanding of these genetic influences can also reveal connections between conditions and guide more integrated monitoring. For example, common genetic variants have been associated with both serum urate levels and the risk of gout, as well as influencing lipid concentrations and the risk of coronary artery disease.^[26]These findings point to overlapping genetic pathways that contribute to different metabolic and disease phenotypes, such as the frequent comorbidity of dyslipidemia and hyperuricemia. Integrating genetic information could thus facilitate the early detection of these interconnected conditions, leading to more holistic and effective management strategies for patients presenting with complex metabolic profiles.^[26]

Elucidating Disease Pathways and Broadening Clinical Understanding

The identification of numerous genetic loci influencing a broad spectrum of biomarker traits, including those indicative of subclinical atherosclerosis, significantly contributes to a deeper understanding of disease pathways and their interconnections. Genetic associations have been found with quantitative measures of subclinical atherosclerosis, such as the ankle-brachial index, coronary artery calcification, and carotid artery intima media thickness.^[2]Since these markers are established predictors of future cardiovascular events, their genetic determinants provide valuable insights into the underlying biological mechanisms of atherosclerosis progression and its complications.^[2] Such discoveries are crucial for elucidating the genetic architecture of complex, multifactorial diseases and can highlight shared genetic bases for conditions that may initially appear distinct.

This broader understanding of genetic associations can substantially improve the clinical comprehension of disease progression and related conditions. For instance, the recognition that common variants at multiple loci contribute to polygenic dyslipidemia, affecting various lipid parameters, emphasizes the intricate interplay of genetic factors in metabolic health.^[27]While many of these findings originate from studies predominantly involving middle-aged to elderly individuals of European descent, requiring replication in diverse populations, the consistent identification of these genetic links across multiple cohorts underscores their potential generalizability in understanding disease etiology and progression.^[1] This knowledge is fundamental for developing novel therapeutic targets and refining existing clinical guidelines, ultimately enhancing the management of patients with complex, interconnected health issues.

References

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[2] O’Donnell, Christopher J., et al. “Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI’s Framingham Heart Study.”BMC Med Genet, 2007.

[3] Benyamin, Beben, et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”American Journal of Human Genetics, vol. 84, no. 1, 2009, pp. 60-65. PubMed, PMID: 19084217.

[4] 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, suppl. 1, 2007, p. S10. PubMed, PMID: 17903294.

[5] Vasan, Ramachandran 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, p. S5. PubMed, PMID: 17903301.

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[7] Willer, C. J., et al. (2008). Newly identified loci that influence lipid concentrations and risk of coronary artery disease.Nat Genet, 40(2), 161–169.

[8] Libra, M, et al. “Analysis of G(-174)C IL-6 polymorphism and plasma concentrations of inflammatory markers in patients with type 2 diabetes and peripheral arterial disease.”J Clin Pathol, 2006.

[9] Cardellini, M, et al. “C-174G polymorphism in the promoter of the interleukin-6 gene is associated with insulin resistance.”Diabetes Care, 2005.

[10] Vitart, V, et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nat Genet, 2008.

[11] Doring, A, et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, 2008.

[12] Kooner, Jaspal S., et al. “Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides.” Nat Genet, 2008.

[13] Chambers, JC, et al. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nat Genet, 2008.

[14] Malerba, Giovanni, et al. “SNPs of the FADS Gene Cluster are Associated with Polyunsaturated Fatty Acids in a.” Hum Mol Genet, 2008.

[15] Gieger, C., et al. (2008). Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.PLoS Genet, 4(11), e1000282.

[16] McArdle, P. F., et al. (2008). Association of a common nonsynonymous variant in GLUT9 with serum uric acid levels in old order amish.Arthritis Rheum, 58(9), 2874–2881.

[17] Saxena, R., et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, 2007.

[18] Edwards, P. A., et al. “Improved methods for the solubilization and assay of hepatic 3-hydroxy-3-methylglutaryl coenzyme A reductase.” J Lipid Res, 1979.

[19] Yuan, X et al. “Population-based genome-wide association studies reveal six loci influencing plasma levels of liver enzymes.” Am J Hum Genet, 2008.

[20] Menzel, S., et al. “A QTL influencing F cell production maps to a gene encoding a zinc-finger protein on chromosome 2p15.” Nat Genet, 2007.

[21] Schaeffer, L., et al. “Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids.” Hum Mol Genet, 2006.

[22] Aulchenko, YS et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, 2008.

[23] Cirillo, P., Sato, W., Reungjui, S., Heinig, M., Gersch, M., et al. (2006). Uric Acid, the metabolic syndrome, and renal disease.J Am Soc Nephrol, 17(12 Suppl 3), S165–S168.

[24] Döring, A., Gieger, C., Mehta, D., Gohlke, H., Prokisch, H., et al. (2008). SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.Nat Genet, 40(4), 430–436.

[25] Enomoto, A., Kimura, H., Chairoungdua, A., Shigeta, Y., Jutabha, P., et al. (2002). Molecular identification of a renal urate anion exchanger that regulates blood urate levels.Nature, 417(6887), 447–452.

[26] Wallace, C et al. “Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia.”Am J Hum Genet, 2008.

[27] Kathiresan, S et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008.