Endosulfan
Background
Section titled “Background”Endosulfan is an organochlorine insecticide and acaricide that was historically employed globally in agriculture to manage a wide array of insect and mite pests on various crops, including cotton, coffee, tea, and diverse fruits and vegetables. Its chemical properties, characterized by high environmental persistence, led to substantial ecological and health concerns, ultimately resulting in its widespread phase-out and prohibition in numerous nations.
Biological Basis
Section titled “Biological Basis”As a neurotoxin, endosulfan primarily affects biological systems by disrupting the central nervous system. Its mechanism of action involves acting as a non-competitive antagonist of the gamma-aminobutyric acid (GABA) receptor-chloride ion channel complex. By binding to a specific site on the GABA receptor, endosulfan inhibits the normal influx of chloride ions into neurons. This interference prevents GABA, an inhibitory neurotransmitter, from performing its function effectively, leading to neuronal hyperexcitation and characteristic symptoms such as tremors, hyperactivity, and convulsions.
Clinical Relevance
Section titled “Clinical Relevance”Acute exposure to endosulfan in humans can manifest as severe neurological symptoms, including seizures, convulsions, headaches, nausea, and vomiting, with extreme cases potentially leading to respiratory failure and fatality. Long-term or chronic exposure has been linked to a spectrum of adverse health effects, such as developmental toxicity, reproductive disorders, and endocrine disruption, due to its capacity to interfere with the body’s hormonal systems. Research into its potential carcinogenic properties is ongoing.
Social Importance
Section titled “Social Importance”The extensive historical use of endosulfan, coupled with its persistence in environmental matrices like soil and water and its propensity for bioaccumulation through the food chain, has generated significant social and environmental implications. Its presence in ecosystems can adversely impact non-target organisms, including wildlife and beneficial insect populations. Human exposure pathways include dietary consumption of contaminated produce, occupational exposure for agricultural workers, and residential proximity to treated areas. The health risks associated with endosulfan, especially among susceptible populations, have driven international agreements and national legislative actions to restrict or ban its manufacture and application, underscoring the critical balance between agricultural practices, public health, and environmental stewardship.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”The ability to identify definitive genetic associations for complex traits is often constrained by study design and statistical power. Many studies using genome-wide association approaches suffer from moderate sample sizes, which limit their power to detect genetic effects that explain a modest proportion of phenotypic variation, increasing the risk of false-negative findings. [1] Furthermore, the extensive number of statistical tests performed in a genome-wide scan raises the probability of reporting false-positive associations, notwithstanding some findings being biologically plausible candidates. [2] The replication of initial findings in independent cohorts is therefore crucial for validation, as a significant portion of reported associations may not be reproducible due to varying study populations or inadequate statistical power. [2]
Another limitation stems from the coverage of genetic variation and genotyping accuracy. Early genome-wide association studies (GWAS) utilized chips with partial coverage of genetic variation, which may be insufficient to capture all true associations within specific gene regions. [2] While newer, denser arrays offer improved coverage, the imputation of missing genotypes to enhance comparability across studies or broaden coverage can introduce a small margin of error, typically around 1.5-2% per allele. [3]Additionally, stringent filtering criteria for single nucleotide polymorphisms (SNPs), such as those related to minor allele frequency, Hardy-Weinberg equilibrium, and genotyping call rates, are necessary to maintain data quality but can inadvertently exclude potentially informative variants.[4]
Phenotype Characterization and Environmental Factors
Section titled “Phenotype Characterization and Environmental Factors”The precise characterization of phenotypes is critical, and limitations in measurement can significantly impact genetic association findings. For instance, averaging echocardiographic traits over extended periods, sometimes spanning decades and involving different equipment, can introduce misclassification and mask age-dependent genetic effects. [2]This approach assumes that the underlying genetic and environmental influences on traits remain consistent across a wide age range, an assumption that may not always hold true. Similarly, certain biomarkers used as indicators of health, such as cystatin C for kidney function or TSH for thyroid function, may not be entirely specific to their intended physiological role or may lack comprehensive supporting measures like free thyroxine, potentially confounding interpretations of genetic associations.[5]
Furthermore, complex traits are often influenced by intricate interactions between genes and the environment, which are frequently not investigated in initial GWAS. Environmental factors, such as dietary intake, time of blood collection, or menopausal status, can modulate how genetic variants influence a phenotype. [2] The absence of a detailed examination of these gene-environment interactions limits the comprehensive understanding of the genetic architecture of a trait, suggesting that observed associations might be context-specific and not universally applicable. [2] The use of multivariable models, while accounting for known confounders, may also inadvertently overlook significant bivariate associations between SNPs and phenotypes. [5]
Generalizability and Study Population Specificity
Section titled “Generalizability and Study Population Specificity”A notable limitation in many genetic studies is the restricted diversity of the study populations, which frequently consist predominantly of individuals of white European ancestry. [2] This lack of ethnic diversity means that findings may not be generalizable to other racial or ethnic groups, as genetic architectures and environmental exposures can vary significantly across different populations. [2] Moreover, cohorts often comprise middle-aged to elderly participants, which limits the applicability of findings to younger individuals and may introduce a survival bias, as DNA collection at later examinations could select for healthier individuals who lived longer. [1] Therefore, the interpretation of identified genetic associations must consider these population-specific characteristics, acknowledging that the broader relevance of these findings requires further investigation in more diverse and representative cohorts.
Variants
Section titled “Variants”Genetic variations play a crucial role in determining an individual’s susceptibility and response to environmental agents, including pesticides like endosulfan. Understanding variants in genes involved in cellular signaling, metabolic pathways, and detoxification processes can shed light on differential health outcomes following exposure. The listed variants cover a spectrum of functions, from core cellular regulation to developmental pathways, which collectively contribute to the body’s overall resilience against environmental stressors.
The PDE4D gene, associated with the variant rs10491442 , encodes phosphodiesterase 4D, an enzyme vital for regulating cyclic adenosine monophosphate (cAMP) levels within cells. cAMP signaling is a fundamental pathway involved in numerous physiological processes, including inflammation, immune responses, and neurotransmission. Variations inPDE4Dcan influence these pathways, potentially altering an individual’s cellular stress response and inflammatory cascades when exposed to environmental toxins such as endosulfan, which is known to affect neurological and endocrine systems.[6] Similarly, the CDC14A gene, linked to rs17122597 , produces a phosphatase essential for proper cell cycle progression and centrosome separation. Effective cell cycle control is critical for repairing cellular damage and maintaining genomic stability, and disruptions caused by variants in CDC14A could impair the body’s ability to respond to damage induced by pesticides, potentially increasing vulnerability to their long-term health consequences. [7]
Variants affecting developmental and structural integrity genes also contribute to individual responses to environmental exposures. For instance, rs114726772 in the USH2Agene relates to a protein critical for the development and maintenance of sensory cells in the inner ear and retina, influencing structural integrity and cell adhesion. While primarily associated with sensory disorders, its role in maintaining cellular architecture suggests that variants might indirectly impact the resilience of various tissues against general environmental stressors, potentially exacerbating damage from neurotoxic agents like endosulfan.[8] Another key player, FGF12 (Fibroblast Growth Factor 12), with variant rs72607877 , is involved in neuronal function, specifically in modulating voltage-gated sodium channels crucial for nerve impulse transmission. Genetic variations inFGF12could therefore influence neuronal excitability and resilience, potentially modulating an individual’s susceptibility to the neurotoxic effects of endosulfan, a known disruptor of nervous system function .
Other significant variants include rs8021014 in COX16, a component of the SYNJ2BP-COX16 locus. The COX16 gene plays a vital role in mitochondrial respiration, specifically in the assembly of cytochrome c oxidase, a crucial enzyme for cellular energy production. Mitochondrial dysfunction is a common pathway for toxicity from environmental pollutants, including pesticides, which often induce oxidative stress and impair energy metabolism. Variations in COX16could impact mitochondrial efficiency, rendering individuals more susceptible to metabolic disruptions from endosulfan exposure. Similarly,rs7607266 in COMMD1 (Copper Metabolism MURR1 Domain Containing 1) is relevant given its role in copper homeostasis and NF-κB signaling, a central inflammatory pathway. Since copper dysregulation and inflammation are often linked to pesticide toxicity, COMMD1variants could affect the body’s management of metal toxicity or inflammatory responses, influencing susceptibility to endosulfan’s systemic adverse effects.[9] The PLPPR1 gene, associated with rs7867688 , is involved in lipid metabolism and neuronal development. Alterations in lipid signaling due to this variant might impact neuronal health and membrane integrity, potentially increasing vulnerability to neurotoxins, as environmental chemicals often disrupt lipid balance .
Finally, the TSHZ2 gene, with variant rs6022454 , encodes a transcription factor crucial for developmental processes, including nervous system development. Variations in such developmental genes can subtly influence the formation and function of tissues, potentially altering an individual’s inherent robustness when faced with environmental challenges like endosulfan. Additionally, variantsrs72942461 in LINC00607 and rs115347967 in LINC02462 - EEF1A1P35highlight the importance of non-coding RNA genes. Long intergenic non-coding RNAs (lncRNAs) are known to regulate gene expression broadly, impacting chromatin structure, transcription, and RNA processing. Variations in these regulatory regions can indirectly affect metabolic capacities or cellular stress responses, which are essential for detoxifying and repairing damage caused by environmental contaminants like endosulfan ;.[6]
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 |
Biological Background
Section titled “Biological Background”There is no information about ‘endosulfan’ in the provided context.
References
Section titled “References”[1] Benjamin, Emelia J., et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Medical Genetics, 2007.
[2] 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, 2007, vol. 8, no. Suppl 1, p. S2.
[3] Willer, Cristen J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nature Genetics, 2008.
[4] Dehghan, Abbas, et al. “Association of three genetic loci with uric acid concentration and risk of gout: a genome-wide association study.”Lancet, 2008.
[5] Hwang, S. J., et al. “A genome-wide association for kidney function and endocrine-related traits in the NHLBI’s Framingham Heart Study.” BMC Med Genet, 2007, vol. 8, no. Suppl 1, p. S10.
[6] Melzer, D., et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genet, 2008, vol. 4, no. 5, p. e1000072.
[7] O’Donnell, C. 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, vol. 8, no. Suppl 1, p. S11.
[8] Wilk, J. B., et al. “Framingham Heart Study genome-wide association: results for pulmonary function measures.” BMC Med Genet, 2007, vol. 8, no. Suppl 1, p. S8.
[9] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008, vol. 40, no. 12, pp. 1417-1424.