Dieldrin
Introduction
Section titled “Introduction”Dieldrin is a highly persistent organochlorine insecticide that was widely used globally from the 1950s to the 1970s for agricultural pest control, particularly against soil insects and as a seed treatment. Chemically related to aldrin, which it is a metabolic product of, dieldrin is known for its remarkable stability in the environment and its broad-spectrum toxicity. Its use significantly declined and was eventually banned in most countries due to growing concerns about its environmental impact and potential health risks to humans and wildlife.
Biological Basis
Section titled “Biological Basis”Dieldrin exerts its toxic effects primarily by acting as a neurotoxin. Its main mechanism of action involves interfering with the central nervous system, specifically by antagonizing the gamma-aminobutyric acid (GABA-A) receptor. GABA is the primary inhibitory neurotransmitter in the mammalian brain. By blocking GABA’s action, dieldrin causes hyperexcitation of the nervous system, leading to uncontrolled neuronal firing. This disruption can manifest as tremors, convulsions, and seizures. Dieldrin is also highly lipophilic, meaning it readily accumulates in fatty tissues, which contributes to its persistence and ability to bioaccumulate up the food chain.
Clinical Relevance
Section titled “Clinical Relevance”Exposure to dieldrin, whether acute or chronic, can have significant clinical consequences. Acute poisoning, often resulting from accidental or occupational exposure, can lead to symptoms such as headaches, dizziness, nausea, vomiting, muscle twitching, and in severe cases, convulsions, coma, and even death. Chronic exposure has been linked to a range of health issues, including neurological disorders, liver damage, reproductive problems, and an increased risk of certain cancers. Due to its persistence, even low-level exposure over long periods can pose a risk as the compound accumulates in the body.
Social Importance
Section titled “Social Importance”The legacy of dieldrin highlights critical aspects of environmental health and public policy. Its widespread use, followed by its eventual ban, underscores the global challenge of balancing agricultural productivity with environmental protection and human health. Dieldrin’s persistence in soil and water, its ability to bioaccumulate in the food chain, and its long half-life in biological systems mean that it continues to be detected in the environment and in human populations decades after its cessation. This has led to ongoing monitoring efforts and international agreements aimed at managing and eliminating persistent organic pollutants (POPs), of which dieldrin is a prime example. The story of dieldrin serves as a cautionary tale regarding the long-term consequences of persistent chemical use and emphasizes the importance of thorough toxicological and ecological assessments before widespread deployment of new compounds.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Research into complex biomarker traits often faces inherent methodological and statistical limitations that can influence the robustness and generalizability of findings. The moderate sample sizes in some cohorts may lead to insufficient statistical power to detect associations with modest effect sizes, increasing the risk of false negative findings. [1] Conversely, a common challenge in genome-wide association studies is the potential for false positive findings arising from the extensive multiple comparisons performed. [1] Replication efforts are crucial, yet many reported associations may not consistently replicate across independent studies, possibly due to false positives in initial reports or subtle differences in cohort characteristics that modify genotype-phenotype relationships. [1]
Furthermore, the integrity of genotype data can be compromised by imputation errors, with reported error rates ranging from 1.46% to 2.14% per allele, and some specific imputed variants showing very low quality estimates. [2] Variations in genotyping platforms across different cohorts can introduce inconsistencies in SNP coverage and data quality, potentially affecting meta-analyses. [3] Analytical inconsistencies, such as differing covariate adjustments (e.g., for age-squared or diabetes status) or variable exclusion criteria for outliers and individuals on medication, can complicate the standardization and interpretation of results when combining data from multiple studies. [4]
Population Specificity and Phenotype Assessment
Section titled “Population Specificity and Phenotype Assessment”A significant limitation of many genetic association studies is the restricted generalizability of their findings, as cohorts are frequently composed predominantly of individuals of European ancestry. [4] This homogeneity limits the applicability of discovered genetic associations to individuals from other ethnic or racial backgrounds, or even to different age groups, such as younger populations. [1] Such population stratification necessitates further research in diverse populations to confirm the universality of identified genetic effects.
Phenotype assessment also presents challenges, with inconsistencies noted in how traits are measured and adjusted across different studies. While efforts are made to standardize adjustments for variables like age and sex, specific covariates such as lipid-lowering therapy or diabetes status are not always uniformly available or considered across all cohorts. [4] The timing of DNA collection in relation to phenotype assessment, particularly in longitudinal studies, can introduce survival bias, as only individuals who have survived to later examinations are included in the genetic analyses. [1] These variations can impact the comparability of results and the overall interpretation of genetic contributions to complex traits.
Unexplored Genetic Architecture and Confounding Factors
Section titled “Unexplored Genetic Architecture and Confounding Factors”The current understanding of the genetic architecture of complex traits remains incomplete, as many studies primarily focus on common genetic variants and typically assume an additive model of inheritance. [4] This approach may overlook the contributions of rare variants, which are often excluded from analyses due to low minor allele frequency, and may not fully capture more complex genetic interactions or non-additive effects. [5] Conditional analyses have shown that many initially significant associations at a locus may not represent independent signals, indicating a more intricate genetic landscape than initially apparent and suggesting that a single lead SNP often reflects a broader region of association. [4]
Environmental and gene-environment confounders also pose challenges, as these factors are not always comprehensively measured or accounted for in study designs. For instance, the lack of consistent information on relevant environmental or treatment variables, such as the use of lipid-lowering therapy, can obscure the true genetic effects or introduce residual confounding. [4] While some studies make efforts to adjust for known covariates, the potential for unmeasured environmental factors or complex gene-environment interactions to influence trait variation suggests that a significant portion of heritability may still be unaccounted for, highlighting persistent knowledge gaps in the complete etiology of complex traits.
Variants
Section titled “Variants”Genetic variations play a crucial role in individual susceptibility and response to environmental exposures, including persistent organic pollutants like dieldrin. These single nucleotide polymorphisms (SNPs) and their associated genes are implicated in diverse biological pathways, from cellular signaling and metabolism to neurological development and gene expression regulation, which can collectively influence an organism’s ability to process or be affected by toxins.[6]Understanding these variants helps elucidate the complex interplay between genetics and environmental factors in health and disease.[7]
The PDE4D gene, encoding a phosphodiesterase, is critical for regulating cyclic AMP (cAMP) levels, an important signaling molecule involved in numerous cellular processes, including inflammation, immune responses, and neurotransmission. A variant like rs10491442 could potentially alter the enzyme’s activity, thereby affecting the balance of cAMP-mediated signaling pathways. [6] Similarly, CDC14A is a phosphatase involved in cell cycle progression and cellular stress responses, with its product influencing cell division and DNA repair mechanisms; rs17122597 might modulate these fundamental cellular processes, impacting how cells respond to damage or proliferation signals. [8] COMMD1 participates in copper homeostasis, NF-κB signaling, and protein degradation, all vital for cellular resilience. The variant rs7607266 could influence these regulatory functions, potentially altering the body’s detoxification capacity or inflammatory reactions. Given dieldrin’s known impact on neurological function and cellular stress pathways, variations in these genes could modify an individual’s vulnerability to its toxic effects by affecting cellular signaling fidelity, cell cycle control, and the management of oxidative stress.
USH2A is a large gene primarily associated with Usher syndrome, affecting both hearing and vision through its role in the development and maintenance of sensory hair cells and photoreceptors. While rs114726772 specific effects are not detailed, variants in this gene can disrupt the structural integrity or function of these specialized cells, potentially leading to sensory impairments. [9] FGF12 encodes a fibroblast growth factor, crucial for neuronal excitability and the maintenance of nerve impulse conduction, suggesting a role in neurological health and development. A variant such as rs72607877 may influence neuronal signaling or structural integrity, which could be particularly relevant in the context of neurotoxic exposures. [10] PLPPR1 (phospholipid phosphatase-related protein 1) is involved in lipid signaling and cell adhesion, playing roles in neuronal development and migration. The variant rs7867688 might affect how cells interact with their environment or how lipid signaling pathways are regulated, which are often targets of environmental toxins. Dieldrin is a known neurotoxicant; thus, genetic variations inUSH2A, FGF12, and PLPPR1 could modulate the nervous system’s susceptibility to its damaging effects, potentially influencing sensory perception, nerve impulse transmission, and neuronal cell survival.
The SYNJ2BP-COX16 gene cluster involves COX16, which is important for the assembly and function of cytochrome c oxidase, a key enzyme in the mitochondrial electron transport chain and cellular energy production. The rs8021014 variant could affect mitochondrial efficiency or cellular respiration, influencing metabolic health and the cell’s ability to cope with energy demands. [11] TSHZ2 is a transcription factor involved in developmental processes and cell differentiation, suggesting a broad role in tissue formation and regulation. Variations like rs6022454 might alter gene expression patterns critical for development or tissue maintenance. [12] Furthermore, long intergenic non-coding RNAs (lincRNAs) such as LINC00607 (rs72942461 ) and LINC02462 - EEF1A1P35 (rs115347967 ) are increasingly recognized for their regulatory roles in gene expression, influencing diverse biological processes without coding for proteins themselves. [6] These non-coding RNA variants could impact the expression of nearby or distant genes, thereby affecting cellular responses to stress or the regulation of metabolic pathways. [13]Given dieldrin’s potential to disrupt cellular energy metabolism and act as an endocrine disruptor, variants in genes likeCOX16 affecting mitochondrial function, TSHZ2 influencing development, and lincRNAs regulating gene expression, could alter an individual’s metabolic resilience or developmental outcomes following exposure.
There is no information about dieldrin in the provided context.
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10491442 | PDE4D | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs17122597 | CDC14A | environmental exposure measurement chlorpyrifos measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs114726772 | USH2A | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement |
| rs72607877 | FGF12 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs8021014 | SYNJ2BP-COX16, COX16 | cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs6022454 | TSHZ2 | cadmium chloride measurement chlorpyrifos measurement azinphos methyl measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs7607266 | COMMD1 | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement |
| rs72942461 | LINC00607 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement 2,4,5-trichlorophenol measurement |
| rs7867688 | PLPPR1 | lipid measurement cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement |
| rs115347967 | LINC02462 - EEF1A1P35 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
Diagnosis
Section titled “Diagnosis”The provided research materials do not contain information pertaining to dieldrin. Therefore, a biological background section for dieldrin cannot be generated from the given context.
References
Section titled “References”[1] Benjamin, E. J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, 2007, p. 58.
[2] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, vol. 40, no. 2, 2008, pp. 161-69.
[3] Aulchenko, Y. S., et al. “Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.”Nat Genet, vol. 41, no. 1, 2009, pp. 47-55.
[4] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, vol. 41, no. 1, 2009, pp. 56-65.
[5] 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. 1823-31.
[6] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, vol. 4, no. 5, 2008, e1000072.
[7] Gieger, Christian, et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genetics, vol. 4, no. 11, 2008, p. e1000282.
[8] Sabatti, Chiara, et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nature Genetics, vol. 40, no. 11, 2008, pp. 1310-18.
[9] Hwang, Shih-Jen, et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S10.
[10] 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 Medical Genetics, vol. 8, no. Suppl 1, 2007, p. S12.
[11] Saxena, Richa, et al. “Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels.”Science, vol. 316, no. 5829, 2007, pp. 1331-36.
[12] 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, no. Suppl 1, 2007, p. S9.
[13] Chambers, John C., et al. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nature Genetics, vol. 40, no. 6, 2008, pp. 716-18.