Azinphos Methyl
Introduction
Section titled “Introduction”Azinphos methyl is an organophosphate insecticide that was historically used extensively in agriculture worldwide to control a broad spectrum of insect pests on various fruit, vegetable, and field crops. Its effectiveness in pest management contributed to crop protection and yield stability. However, due to its significant toxicity to humans and wildlife, its use has been severely restricted or banned in many countries over recent decades.
Biological Basis
Section titled “Biological Basis”The biological basis of azinphos methyl’s action lies in its classification as an acetylcholinesterase inhibitor. In the nervous system, acetylcholinesterase is an enzyme crucial for breaking down the neurotransmitter acetylcholine, which transmits signals between nerve cells and muscle cells. Azinphos methyl, like other organophosphates, irreversibly binds to and inactivates acetylcholinesterase. This inhibition leads to an excessive accumulation of acetylcholine at synaptic junctions, causing continuous stimulation of nerve cells. This overstimulation disrupts normal nerve function throughout the body, affecting both the central and peripheral nervous systems.
Clinical Relevance
Section titled “Clinical Relevance”Exposure to azinphos methyl can result in acute poisoning, presenting a serious clinical concern. Symptoms of poisoning typically manifest rapidly and can range from mild to severe, depending on the dose and duration of exposure. Common signs include nausea, vomiting, diarrhea, abdominal cramps, increased salivation and sweating, constricted pupils (miosis), muscle tremors, weakness, and respiratory distress. In severe cases, it can lead to seizures, loss of consciousness, respiratory failure, and potentially death. Long-term or chronic exposure, even at lower levels, may also be associated with neurological and developmental effects. Medical management of azinphos methyl poisoning often involves administering atropine and pralidoxime chloride to counteract the effects of acetylcholine overstimulation.
Social Importance
Section titled “Social Importance”Azinphos methyl holds considerable social importance due to its former widespread application in agriculture and the subsequent public health and environmental debates surrounding its toxicity. While it played a role in enhancing agricultural productivity, concerns about occupational exposure for farmworkers, potential contamination of food and water supplies, and adverse impacts on non-target organisms, including pollinators and aquatic life, led to its regulatory phase-out. The case of azinphos methyl exemplifies the complex societal challenge of balancing the economic benefits of pest control with the imperative to protect human health and environmental integrity. Its history underscores the ongoing need for rigorous evaluation and regulation of chemical pesticides.
Limitations
Section titled “Limitations”Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”The studies involved relatively modest sample sizes for genome-wide association studies (GWAS), with one analysis including 411 individuals from 150 nuclear families and another focused on 459 female monozygotic twin pairs. [1] Such sample sizes may limit the statistical power to detect genetic variants with small effect sizes or those with lower allele frequencies, potentially leading to an underestimation of the total genetic contribution to the phenotypes examined. Furthermore, the within-family association test, while robust to population stratification, was noted to have limited power compared to total association tests, which could result in missed associations. [1]
The analyses primarily utilized an additive genetic model, which assumes a linear relationship between the number of risk alleles and the phenotypic outcome. [1]While widely used, this model may not fully capture more complex genetic architectures, such as dominant, recessive, or epistatic interactions, which could contribute to the observed variation in serum iron, serum transferrin, transferrin saturation, and serum ferritin levels. Additionally, stringent outlier removal criteria, where individuals with residuals more than 4 standard deviations from the mean were excluded, could potentially remove data points representing rare but biologically significant genetic effects or extreme phenotypes.[1]
Generalizability and Cohort Specificity
Section titled “Generalizability and Cohort Specificity”A significant limitation arises from the specific nature of the study cohorts, particularly the exclusive use of 459 female monozygotic twin pairs in one of the primary GWAS. [1] This design inherently restricts the direct generalizability of findings to male populations or to the general non-twin population, as genetic effects or their magnitudes might differ across sexes or in individuals without a twin sibling. The absence of reported ancestral information for the cohorts further limits the ability to extrapolate results to diverse ethnic or racial groups, potentially obscuring population-specific genetic architectures or allele frequencies for the studied traits.
Unexplained Variation and Environmental Influences
Section titled “Unexplained Variation and Environmental Influences”While the studies successfully identified variants in TF and HFEthat explain approximately 40% of the genetic variation in serum-transferrin levels, a substantial proportion of genetic and total phenotypic variation remains unexplained.[1] This “missing heritability” suggests the involvement of numerous other genetic factors, including common variants with very small effects, rare variants, structural variations, or epigenetic modifications that were not captured by the genotyping arrays or analytical models employed. Consequently, the full genetic landscape influencing these iron-related traits is likely more complex and multifaceted than currently elucidated.
Although the family-based association model incorporated broad environmental effects common to family members or twins, the specific roles of detailed environmental factors and potential gene-environment interactions were not explicitly investigated. [1]Lifestyle factors, dietary iron intake, specific exposures, and other unmeasured environmental influences can significantly modulate serum iron, transferrin, and ferritin levels, potentially confounding genetic associations or modifying their penetrance. A lack of detailed environmental data means that the interplay between genetic predisposition and external factors remains largely unexplored, representing a key knowledge gap.
Variants
Section titled “Variants”PDE4D (Phosphodiesterase 4D) and FGF12 (Fibroblast Growth Factor 12) play critical roles in cellular communication and development. PDE4D is an enzyme that breaks down cyclic AMP (cAMP), a crucial secondary messenger involved in a wide array of physiological processes, including inflammation, immune response, and neurological functions. Variants in PDE4D, such as rs10491442 , may alter cAMP signaling pathways, potentially affecting how cells respond to environmental stressors or toxins. FGF12 belongs to the fibroblast growth factor family, which are signaling proteins important for cell growth, differentiation, and survival, particularly in the nervous system. Genetic variations like rs72607877 in FGF12could influence neuronal development or resilience, which might be relevant in the context of neurotoxic agents like azinphos methyl. The study of single nucleotide polymorphisms (SNPs) helps reveal how DNA variations can influence human diseases and traits[2]. [3]
CDC14A (Cell Division Cycle 14A), COMMD1 (COMM Domain Containing 1), and PLPPR1 (Phospholipid Phosphatase Related 1) are involved in fundamental cellular processes that could impact an individual’s response to environmental chemicals. CDC14A is a phosphatase involved in cell cycle progression, crucial for maintaining genomic stability and proper cell division. Variants like rs17122597 could subtly alter cell cycle regulation, potentially affecting the repair mechanisms after cellular damage. COMMD1 participates in copper homeostasis, protein degradation, and the regulation of the NF-κB inflammatory pathway, which is central to the body’s response to stress and toxins. A variant like rs7607266 might influence inflammatory responses or detoxification capacities. PLPPR1 is involved in lipid metabolism and cell migration, affecting cell membrane composition and signaling. Alterations caused by rs7867688 could impact cellular integrity or the transport of substances across membranes, which is relevant for the uptake and metabolism of compounds like azinphos methyl. Genome-wide association studies (GWAS) routinely identify such genetic variations linked to various physiological traits and disease susceptibilities ;.[4]
USH2A (Usher Syndrome Type 2A), COX16 (Cytochrome C Oxidase Assembly Factor 16), and TSHZ2 (Teashirt Zinc Finger Homeobox 2) govern diverse biological functions. USH2A encodes a protein essential for the development and maintenance of inner ear and retinal cells, linking it to sensory functions. While primarily associated with inherited hearing and vision loss, variants like rs114726772 could indicate broader roles in cellular structural integrity or stress response in neurosensory tissues. COX16, often found in a gene cluster with SYNJ2BP (SYNJ2BP-COX16), is crucial for the assembly of cytochrome c oxidase, a key enzyme in the mitochondrial electron transport chain. Variants such as rs8021014 could affect mitochondrial function, impacting cellular energy production and increasing susceptibility to oxidative stress, a known mechanism of organophosphate toxicity. TSHZ2 is a transcription factor, meaning it regulates the expression of other genes during development and in mature tissues. A variant like rs6022454 might alter gene expression patterns, influencing cellular responses to toxic exposures or repair mechanisms. Genetic factors are known to influence various complex phenotypes, including protein levels and metabolic profiles [2]. [5]
Long intergenic non-coding RNAs (lncRNAs) such as LINC00607 and LINC02462, along with pseudogenes like EEF1A1P35 (Eukaryotic Translation Elongation Factor 1 Alpha 1 Pseudogene 35), represent elements of the genome with significant regulatory potential. While not encoding proteins, lncRNAs like those affected by rs72942461 (in LINC00607) and rs115347967 (in LINC02462) can influence gene expression through various mechanisms, including chromatin remodeling, transcriptional interference, or post-transcriptional regulation. These regulatory roles mean that variants in lncRNAs can subtly alter cellular pathways, potentially influencing an individual’s susceptibility or resilience to environmental toxins by modulating the expression of genes involved in detoxification or stress response. Pseudogenes, like EEF1A1P35, are generally considered non-functional copies of protein-coding genes, but they can sometimes exert regulatory effects on their functional counterparts or other genes. Genetic variation, including single nucleotide polymorphisms, is a key focus in understanding how individuals differ in their biological responses[3]. [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 |
Pathways and Mechanisms
Section titled “Pathways and Mechanisms”Metabolic Homeostasis and Regulation
Section titled “Metabolic Homeostasis and Regulation”The human body maintains a complex network of metabolic pathways involving essential lipids, carbohydrates, and amino acids, whose homeostasis is crucial for physiological function. [5] Genetic variants can significantly influence these metabolic traits, affecting the concentrations of various endogenous metabolites in serum. [5] For instance, studies have identified associations between genetic markers and profiles of 18 amino acids, nine reducing mono-, di-, and oligosaccharides (such as hexoses, desoxyhexoses, uronic acids, and N-acetylglucosamine), and several biogenic amines, demonstrating the intricate genetic control over broad metabolic processes. [5] These pathways are under tight regulation, involving mechanisms like flux control and the coordinated activity of enzymes to ensure energy metabolism, biosynthesis, and catabolism are balanced.
Lipid and Urate Metabolism
Section titled “Lipid and Urate Metabolism”Specific genetic loci have been identified that play critical roles in regulating the metabolism of lipids and uric acid. TheFADS1 and FADS2 gene cluster, for example, is associated with the composition of fatty acids within phospholipids, highlighting its involvement in lipid biosynthesis and modification. [7] Similarly, variants in the SLC2A9gene are known to influence serum urate concentration and urinary urate excretion, directly affecting urate metabolism and predisposing individuals to conditions like gout.[3] Further, genes such as LIPChave been linked to various metabolic traits, indicating their broader regulatory impact on lipid profiles, including levels of low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides.[8] These pathways are subject to regulatory mechanisms that ensure proper lipid transport and storage, and efficient waste product excretion.
Enzymatic Activity and Protein Modification
Section titled “Enzymatic Activity and Protein Modification”Enzymes are central to metabolic and regulatory pathways, with their activity often modulated by genetic factors and post-translational modifications. For example, the activity of alkaline phosphatase, an enzyme involved in various metabolic processes, is regulated by a chromosomal region containing theAkp2 gene. [9]Beyond simple gene expression, protein modification plays a crucial role; for instance, glycosylphosphatidylinositol-specific phospholipase D has been implicated in conditions like nonalcoholic fatty liver disease, suggesting its enzymatic action and potential post-translational regulation are significant in disease pathology.[9] Additionally, the pharmacogenomics of glutathione S-transferase omega 1 and omega 2 (GST omega 1, GST omega 2) underscore the importance of these enzymes in drug metabolism and detoxification, illustrating how genetic variations can impact protein function and metabolic processing. [10]
Systems-Level Metabolic Integration
Section titled “Systems-Level Metabolic Integration”Metabolic pathways are not isolated but rather form an interconnected network, exhibiting significant crosstalk and hierarchical regulation that leads to emergent physiological properties. The interplay between different pathways is evident in the identification of genetic variants that affect multiple metabolic traits simultaneously, such as those associated with PLEK, ANKRD30A, and PARK2. [5]These genes may influence broader metabolic homeostasis, affecting amino acid, carbohydrate, and lipid profiles, suggesting a systemic integration of metabolic control.[5] The functional readout of these integrated pathways provides a comprehensive understanding of the physiological state, where dysregulation in one pathway can trigger compensatory mechanisms or cascade into broader metabolic disturbances, impacting overall health. [5]
References
Section titled “References”[1] Benyamin, B. “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. 805-810.
[2] Melzer, David et al. “A genome-wide association study identifies protein quantitative trait loci (pQTLs).” PLoS Genetics, vol. 4, no. 5, 2008, p. e1000073.
[3] Vitart, Veronique et al. “SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout.”Nature Genetics, vol. 40, no. 4, 2008, pp. 432-437.
[4] 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. 718-720.
[5] 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.
[6] 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. S12.
[7] 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, vol. 15, no. 10, 2006, pp. 1745-1756.
[8] Kathiresan, Sekar, et al. “Six New Loci Associated with Blood Low-Density Lipoprotein Cholesterol, High-Density Lipoprotein Cholesterol or Triglycerides in Humans.”Nat Genet, vol. 40, no. 2, 2008, pp. 189-197.
[9] Yuan, Xin, 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.
[10] Wilk, J. B., et al. “Framingham Heart Study Genome-Wide Association: Results for Pulmonary Function Measures.” BMC Med Genet, vol. 8 Suppl 1, 2007, p. S8.