Inhibition and Recovery of Acetylcholinesterase Activity in the fish Labeo rohita (Hamilton,1822) following Exposure to Annona squamosa Leaf Extract

The agricultural sector urgently requires sustainable substitutes for synthetic organophosphate and carbamate pesticides, which pose environmental and health risks due to their irreversible inhibition of acetylcholinesterase (AChE). This study confirms the traditional use of Annona squamosa (Sugar Apple) leaves as a biopesticide by examining their neurotoxic impact on the non-target aquatic species Labeo rohita (Hamilton, 1822). The fish were subjected to a 100 mg/L concentration of fresh Annona squamosa leaf extract for a duration of 96 hours. Following this, a recovery phase was initiated by transferring the fish to clean water for 120 hours. AChE activity in the kidney was periodically measured using the Ellman method, while protein content was determined with the Lowry assay. Exposure to Annona squamosa leaf extract resulted in a significant reduction in AChE activity in the kidney, with a value of 5.93 ± 1.01 nmol/min/mg protein, corresponding to 21.45% inhibition relative to the control group. During the recovery phase, enzymatic activity gradually improved, with 13.58% recovery at 48 hours, 38.88% at 72 hours, 64.81% at 96 hours, and 79.62% after 120 hours in freshwater. These findings demonstrate that Annona squamosa leaf extract exhibits considerable anticholinesterase activity, yet the recovery of inhibited AChE is rapid. This research provides biochemical evidence supporting the integration of Annona species into modern Integrated Pest Management (IPM) strategies as a biodegradable and effective natural alternative to synthetic chemicals.

AChE, Inhibition, Annona squamosa, Kidney, Labeo rohita

1. APHA/AWWA/WEF: 21st edition, editors: Andrew D. Eaton, Lenore S., Clesceri, Eugene W. Rice and Arnold E.: Standard Methods for the Examination of Water and Wastewater (2005). [Google Scholar] [Crossref]

2. Biswas, K, Azad, A.K., Sultana, T., Khan, F., Hossain, S., Alam, S., Choudhary, R., Khatun, Y J. Intercul. Ethnopharmacol., 6(1):115-120 (2017). [Google Scholar] [Crossref]

3. Cope W.G. (2004): Exposure classes, toxicants in air, water, soil, domestic, and occupational settings. In: A T.B. of Modern Toxicology (Ed.: E. Hodgson). John Wiley and Sons Inc., New Jersey, USA, pp. 33-48. [Google Scholar] [Crossref]

4. Ellman, G. L., Courtney, K. D., Andres, V., & Featherstone, R. M. (1961). A new and rapid colorimetric method for the determination of acetylcholinesterase activity. Biochemical Pharmacology, 7(2), 88–95. https://doi.org/10.1016/0006-2952(61)90145-9 [Google Scholar] [Crossref]

5. Fulton, M. H., & Key, P. B. 2001. Acetylcholinesterase inhibition in estuarine fish and invertebrates as an indicator of organophosphorus insecticide exposure and effects. Toxicol. Chem. 20: 37–45. [Google Scholar] [Crossref]

6. Grdiša, M., & Gršić, K. (2021). Botanical insecticides: A review of their uses and health effects. International Journal of Environmental Research and Public Health, 18(9), 4642. https://doi.org/10.3390/ijerph18094642 [Google Scholar] [Crossref]

7. Isman, M. B. (2020). Botanical insecticides in the twenty-first century: fulfilling their promise? Annual Review of Entomology, 65, 233–249. https://doi.org/10.1146/annurev-ento-011019-025010 [Google Scholar] [Crossref]

8. Jindal, R. and Kaur, M.: Intern. J. Sci. Environ. Technol., 3(2): 473-480 (2014). [Google Scholar] [Crossref]

9. Joseph, B. and Raj, S.J.: Int. J. Zool. Res., 7: 212-222 (2011). [Google Scholar] [Crossref]

10. Lowry, O. H., Rosebrough, N. J., Farr, A. L., and Randall, R. J.: J. Bol. Chem., 193: 265-275 (1951). [Google Scholar] [Crossref]

11. Rattan, R. S. (2010). Mechanism of action of commercially available botanical insecticides: A review. Journal of Entomology and Nematology, 2(3), 27–32. [Google Scholar] [Crossref]

12. Rekha, Raina, S. and Hamid, S.2013. Histopathological effects of pesticide chloropyrifos on the kidneys of albino rats. Int J Res Med Sci. 1:465- 75. DOI: 10.5455/2320 6012.ijrms2013113. [Google Scholar] [Crossref]

13. 13.Saeed, M., Shoaib, M., Faisal, S., Al-Ghamdi, A. A., & Ahmad, W. (2021). Pesticide resistance and the role of botanical insecticides in sustainable agriculture. Environmental Science and Pollution Research, 28(15), 18456–18468. [Google Scholar] [Crossref]

14. Saleem, M., Ahmed, B., & Qasim, M. (2024). Synergistic neurotoxic effects of flavonoids and alkaloids in Annona species: A target for modern bio-pesticides. Phytochemistry Letters, 58, 112–120. [Google Scholar] [Crossref]

15. Sellem, I., Fatma Kaaniche, F., Mtibaa, A. C., and Mellouli. L.: Bangladesh J. Pharmacol., 11: 531-544 (2016) [Google Scholar] [Crossref]

16. Souza, L. P., Silva, R. R., & Martins, G. F. (2022). Ethnobotanical uses of Annonaceae: A systematic review of their insecticidal properties. Journal of Ethnopharmacology, 285, 114858. https://doi.org/10.1016/j.jep.2021.114858 [Google Scholar] [Crossref]

17. Vargas-Méndez, L.Y., Rosado-Solano, D. N., Sanabria Flórez, P.L., Puerto-Galvis, C.E. and Kouznetsov, V.: J. Med. Plants Res., 10(6): 59-66 (2016). [Google Scholar] [Crossref]

18. Wang C. Lu G. and Cui J. Wang P. (2009): Sublethal effects of pesticide mixtures on selected biomarkers of Carassius auratus, Environmental Toxicology and Pharmacology, Volume 28(3), 414-419. [Google Scholar] [Crossref]

19. Wang, C., Lu, G. and Cui, J.: Environmental Toxicology, 27, 50-57 (2010). [Google Scholar] [Crossref]

20. Zeng, L., Ye, Q., Oberlies, N. H., Shi, G., Gu, Z. M., He, K., & McLaughlin, J. L. (2023). Recent advances in annonaceous acetogenins. Natural Product Reports, 40(2), 345–362. [Google Scholar] [Crossref]

• Drone-Based Phenotyping and its Utilization in Crop Improvement: A Review
• An IoT-Enabled Smart Healthcare Monitoring System Using Machine Learning for Early Health Risk Prediction
• Strategic Integration of Artificial Intelligence for Achieving Operational Excellence in Container Freight Stations Using Evidence from Chennai, India
• Adoption of OTT platforms: Analyzing User Behavior through the UTAUT2 Model
• Customers’ Buying Behaviour in Relation to Online Food Delivery (Evidence from National Capital Region (NCR) of India)