Home Food Science and Human Wellness Article
PDF (3.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript
Show Outline
Figures (4)

Tables (1)
Table 1
Open Access

Chlorantraniliprole exposure aggravates high-fat diet-induced metabolic disorders in mice by regulating gut microbiota and its metabolites

Ting Gua,b,#, Jie Linc,#, Jiani Yanga, William Mumbyc, Quancai Sunc(), Ye Pengb()
School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China
Faculty of Medicine, Macau University of Science and Technology, Macao 999078, China
Department of Health, Nutrition, and Food Sciences, Florida State University, Tallahassee 32306, USA

# These authors contributed equally to this article.

Peer review under responsibility of Beijing Academy of Food Sciences.

Show Author Information

Abstract

Food contaminants, particularly insecticides, are important factors contributing to obesity and other adverse effects. As the most widely used diamide insecticide worldwide, chlorantraniliprole (CP) is ubiquitous in food and the environment. However, the influence of CP on obesity and the gut microbiota remains unknown. In this study, we administered CP/carboxymethyl cellulose sodium to C57BL/6J mice with a high-fat diet (HFD) via gavage for 13 weeks. The CP exposure induced significant increases in body weight gain, fat mass, serum total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C). Moreover, CP aggravated the imbalance in the gut microbiota by increasing the relative abundance of obesity-associated bacteria and reducing the relative abundance of beneficial bacteria. Based on untargeted metabolomics analysis, CP was found to be involved in the regulation of pathways including Alanine, aspartate and glutamate metabolism and Arginine and proline metabolism. Notably, CP exposure primarily induced alterations in microbial metabolites within these pathways, such as L-(+)-aspartic acid and L-glutamic acid. Additionally, individual metabolites, such as the lipid mediator (oleoyl ethanolamide), also demonstrated alterations upon CP exposure. Furthermore, Spearman correlation analysis revealed several noteworthy associations between microbial alterations, metabolite changes, and phenotypes. The results of the study demonstrate a connection between microbiota, metabolites, and the effects of CP exposure on HFD-induced obesity, elucidating the critical role of the gut microbiota and its metabolites in the toxic effects of CP.

Keywords

ChlorantraniliproleObesityMetabolic disorderGut microbiotaUntargeted metabolomics

Electronic Supplementary Material

Download File(s)
fshw-14-1-9250079_ESM1.docx (15.2 KB)
fshw-14-1-9250079_ESM2.xlsx (26.3 KB)
fshw-14-1-9250079_ESM3.pdf (12.5 KB)
fshw-14-1-9250079_ESM4.pdf (326.1 KB)

References

[1]

R.M. Gutgesell, E.E. Tsakiridis, S. Jamshed, et al., Impact of pesticide exposure on adipose tissue development and function, Biochem. J. 477(14) (2020) 2639-2653. https://doi.org/10.1042/BCJ20200324.

Crossref Google Scholar
[2]

N. Chevalier, P. Fenichel, Obesite, diabete de type 2 et perturbateurs endocriniens, Presse. Med. 45(1) (2016) 88-97. https://doi.org/10.1016/j.lpm.2015.08.008.

Crossref Google Scholar
[3]

Z. Meng, S. Yan, W. Sun, et al., Chlorothalonil induces obesity in mice by regulating host gut microbiota and bile acids metabolism via FXR pathways, J. Hazard. Mater. 452 (2023). https://doi.org/10.1016/j.jhazmat.2023.131310.

Crossref Google Scholar
[4]

D.H. Lee, Persistent organic pollutants and obesity-related metabolic dysfunction: focusing on type 2 diabetes, Epidemiol. Health. 34 (2012) 1-3. https://doi.org/10.4178/epih/e2012002.

Crossref Google Scholar
[5]

N.E. De Long, A.C. Holloway, Early-life chemical exposures and risk of metabolic syndrome, Diabetes, Metab. Syndr. Obes. 10 (2017) 101-109. https://doi.org/10.2147/dmso.S95296.

Crossref Google Scholar
[6]

Q. Liu, W. Shao, C. Zhang, et al., Organochloride pesticides modulated gut microbiota and influenced bile acid metabolism in mice, Environ. Pollut. 226 (2017) 268-276. https://doi.org/10.1016/j.envpol.2017.03.068.

Crossref Google Scholar
[7]

X. Xiao, J.M. Clark, Y. Park, Potential contribution of insecticide exposure and development of obesity and type 2 diabetes, Food. Chem. Toxicol. 105 (2017) 456-474. https://doi.org/10.1016/j.fct.2017.05.003.

Crossref Google Scholar
[8]

V. Gaddamidi, M.T. Scott, R.S. Swain, et al., Metabolism of C-14 chlorantraniliprole in the lactating goat, J. Agric. Food. Chem. 59(4) (2011) 1316-1323. https://doi.org/10.1021/jf103558n.

Crossref Google Scholar
[9]

G.P. Lahm, D. Cordova, J.D. Barry, New and selective ryanodine receptor activators for insect control, Bioorg. Med. Chem. 17(12) (2009) 4127-4133. https://doi.org/10.1016/j.bmc.2009.01.018.

Crossref Google Scholar
[10]
Joint FAO-WHO Meeting of Pesticide Residues, Chlorantraniliprole, 2008. Available from: https://www.fao.org/fileadmin/templates/agphome/documents/Pests_Pesticides/JMPR/Report08/Chlorantraniliprole.pdf.
[11]
Environmental Protection Agency, Pesticide fact sheet for chlorantraniliprole, 2008. Available from: https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100C269.txt.
[12]

G. Chen, G. Wang, W. Xu, et al., Transcriptome analysis of fat accumulation in 3T3-L1 adipocytes induced by chlorantraniliprole, Front. Nutr. 9 (2022). https://doi.org/10.3389/fnut.2022.1091477.

Crossref Google Scholar
[13]

L. Yuan, J. Lin, Y. Peng, et al., Chlorantraniliprole induces adipogenesis in 3T3-L1 adipocytes via the AMPKalpha pathway but not the ER stress pathway, Food. Chem. 311 (2020) 125953. https://doi.org/10.1016/j.foodchem.2019.125953.

Crossref Google Scholar
[14]

L. Li, Y. Zhang, J.R. Speakman, et al., The gut microbiota and its products: establishing causal relationships with obesity related outcomes, Obes. Rev. 22(12) (2021). https://doi.org/10.1111/obr.13341.

Crossref Google Scholar
[15]

J.K. Jo, S.H. Seo, S.E. Park, et al., Gut microbiome and metabolome profiles associated with high-fat diet in mice, Metabolites 11(8) (2021) 482. https://doi.org/10.3390/metabo11080482.

Crossref Google Scholar
[16]

J. Liu, F. Zhao, T. Wang, et al., Host metabolic disorders induced by alterations in intestinal flora under dietary pesticide exposure, J. Agric. Food. Chem. 69(22) (2021) 6303-6317. https://doi.org/10.1021/acs.jafc.1c00273.

Crossref Google Scholar
[17]

X. Yuan, Z. Pan, C. Jin, et al., Gut microbiota: an underestimated and unintended recipient for pesticide-induced toxicity, Chemosphere 227 (2019) 425-434. https://doi.org/10.1016/j.chemosphere.2019.04.088.

Crossref Google Scholar
[18]

Y. Jin, S. Wu, Z. Zeng, et al., Effects of environmental pollutants on gut microbiota, Environ. Pollut. 222 (2017) 1-9. https://doi.org/10.1016/j.envpol.2016.11.045.

Crossref Google Scholar
[19]

European Food Safety Authority, Conclusion on the peer review of the pesticide risk assessment of the active substance chlorantraniliprole, EFSA J. 2013. 11(6) (2013) 3143. https://doi.org/10.2903/j.efsa.2013.3143.

Crossref Google Scholar
[20]

B.N. Liu, X.T. Liu, Z.H. Liang, et al., Gut microbiota in obesity, World. J. Gastroenterol. 27(25) (2021) 3837-3850. https://doi.org/10.3748/wjg.v27.i25.3837.

Crossref Google Scholar
[21]

Q. Sun, J. Lin, Y. Peng, et al., Flubendiamide enhances adipogenesis and inhibits AMPKα in 3T3-L1 Adipocytes, Molecules 23(11) (2018) 2950. https://doi.org/10.3390/molecules23112950.

Crossref Google Scholar
[22]

J. Kim, J.H. Choi, T. Oh, et al., Codium fragile ameliorates high-fat diet-induced metabolism by modulating the gut microbiota in mice, Nutrients 12(6) (2020). https://doi.org/10.3390/nu12061848.

Crossref Google Scholar
[23]

Y. Gupta, A.L. Ernst, A. Vorobyev, et al., Impact of diet and host genetics on the murine intestinal mycobiome, Nat. Commun. 14(1) (2023) 834. https://doi.org/10.1038/s41467-023-36479-z.

Crossref Google Scholar
[24]

Y.T. Chen, S.Y. Chiou, A.H. Hsu, et al., Lactobacillus rhamnosus strain LRH05 intervention ameliorated body weight gain and adipose inflammation via modulating the gut microbiota in high-fat diet-induced obese mice, Mol. Nutr. Food. Res. 66(1) (2022) 348. https://doi.org/10.1002/mnfr.202100348.

Crossref Google Scholar
[25]

L. Zhu, J. Fu, X. Xiao, et al., Faecal microbiota transplantation-mediated jejunal microbiota changes halt high-fat diet-induced obesity in mice via retarding intestinal fat absorption, Microb. Biotechnol. 15(1) (2022) 337-352. https://doi.org/10.1111/1751-7915.13951.

Crossref Google Scholar
[26]

Z. Lv, C. Zhang, W. Song, et al., Jellyfish collagen hydrolysate alleviates inflammation and oxidative stress and improves gut microbe composition in high-fat diet-fed mice, Mediators. Inflamm. 2022 (2022) 1-8. https://doi.org/10.1155/2022/5628702.

Crossref Google Scholar
[27]

J. Kim, H. Lee, J. An, et al., Alterations in gut microbiota by statin therapy and possible intermediate effects on hyperglycemia and hyperlipidemia, Front. Microbiol. 10 (2019) 1947. https://doi.org/10.3389/fmicb.2019.01947.

Crossref Google Scholar
[28]

Q. Zeng, D. Li, Y. He, et al., Discrepant gut microbiota markers for the classification of obesity-related metabolic abnormalities, Sci. Rep. 9 (2019) 13424. https://doi.org/10.1038/s41598-019-49462-w.

Crossref Google Scholar
[29]

H. Mei, N. Li, Y. Zhang, et al., Gut microbiota diversity and overweight/obesity in infancy: results from a nested case-control study, Curr. Med. Sci. 42(1) (2022) 210-216. https://doi.org/10.1007/s11596-021-2476-1.

Crossref Google Scholar
[30]

M. Li, Z. Zhang, B. Yu, et al., Lycium barbarum oligosaccharides alleviate hepatic steatosis by modulating gut microbiota in C57BL/6J mice fed a high-fat diet, Foods 12(8) (2023) 1617. https://doi.org/10.3390/foods12081617.

Crossref Google Scholar
[31]

Z.M. Zhang, M.J. Chen, J.F. Zou, et al., UPLC-Q-TOF/MS based fecal metabolomics reveals the potential anti-diabetic effect of Xiexin decoction on T2DM rats, J. Chromatogr. B 1173 (2021) 122683. https://doi.org/10.1016/j.jchromb.2021.122683.

Crossref Google Scholar
[32]

C. Takashina, I. Tsujino, T. Watanabe, et al., Associations among the plasma amino acid profile, obesity, and glucose metabolism in Japanese adults with normal glucose tolerance, Nutr. Metab. 13(1) (2016) 5. https://doi.org/10.1186/s12986-015-0059-5.

Crossref Google Scholar
[33]

I. Maltais-Payette, B. Allam-Ndoul, L. Perusse, et al., Circulating glutamate level as a potential biomarker for abdominal obesity and metabolic risk, Nutr. Metab. Cardiovasc. Dis. 29(12) (2019) 1353-1360. https://doi.org/10.1016/j.numecd.2019.08.015.

Crossref Google Scholar
[34]

D. Xie, Y. Zhang, Y. Guo, et al., The impact of high-glucose or high-fat diets on the metabolomic profiling of mice, Front. Nutr. 10 (2023) 1171806. https://doi.org/10.3389/fnut.2023.1171806.

Crossref Google Scholar
[35]

K. Shan, H. Qu, K. Zhou, et al., Distinct gut microbiota induced by different fat-to-sugar-ratio high-energy diets share similar pro-obesity genetic and metabolite profiles in prediabetic mice, mSystems 4(5) (2019) e00219-19. https://doi.org/10.1128/mSystems.00219-19.

Crossref Google Scholar
[36]

N.M. Ammar, M.A. Farag, T.E. Kholeif, et al., Serum metabolomics reveals the mechanistic role of functional foods and exercise for obesity management in rats, J. Pharm. Biomed. Anal. 142 (2017) 91-101. https://doi.org/10.1016/j.jpba.2017.05.001.

Crossref Google Scholar
[37]

R. Ikegami, I. Shimizu, T. Sato, et al., Gamma-aminobutyric acid signaling in brown adipose tissue promotes systemic metabolic derangement in obesity, Cell. Rep. 24(11) (2018) 2827. https://doi.org/10.1016/j.celrep.2018.08.024.

Crossref Google Scholar
[38]

K. SHIBATA, Urinary excretion of 2-oxo acids is greater in rats with streptozotocin-induced diabetes, J. Nutr. Sci. Vitaminol. 64(4) (2018) 292-295. https://doi.org/10.3177/jnsv.64.292.

Crossref Google Scholar
[39]

J. Zhang, C. Yi, J. Han, et al., Dose effect of high-docosahexaenoic acid tuna oil on dysbiosis in high-fat diet mice, J. Sci. Food. Agric. 102(12) (2022) 5531-5543. https://doi.org/10.1002/jsfa.11908.

Crossref Google Scholar
[40]

S. Moran-Ramos, E. Ocampo-Medina, R. Gutierrez-Aguilar, et al., An amino acid signature associated with obesity predicts 2-year risk of hypertriglyceridemia in school-age children, Sci. Rep. 7 (2017) 5607. https://doi.org/10.1038/s41598-017-05765-4.

Crossref Google Scholar
[41]

Q. Li, W. Gu, X. Ma, et al., Amino acid and biogenic amine profile deviations in an oral glucose tolerance test: a comparison between healthy and hyperlipidaemia individuals based on targeted metabolomics, Nutrients 8(6) (2016) 379. https://doi.org/10.3390/nu8060379.

Crossref Google Scholar
[42]

J. Swierczynski, T. Sledzinski, E. Slominska, et al., Serum phenylalanine concentration as a marker of liver function in obese patients before and after bariatric surgery, Obes. Surg. 19(7) (2009) 883-889. https://doi.org/10.1007/s11695-008-9521-z.

Crossref Google Scholar
[43]
National Center for Biotechnology Information. PubChem compound summary for CID 997, phenylpyruvic acid, 2023. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/Phenylpyruvic-acid.
[44]

L. Payahoo, Y. Khajebishak, M.R. Alivand, et al., Investigation the effect of oleoylethanolamide supplementation on the abundance of Akkermansia muciniphila bacterium and the dietary intakes in people with obesity: a randomized clinical trial, Appetite 141 (2019) 104301. https://doi.org/10.1016/j.appet.2019.05.032.

Crossref Google Scholar
[45]

T. Zhang, L.H. Bao, Q. Zhao, et al., Metabolomics reveals gut microbiota contribute to PPARα deficiency-induced alcoholic liver injury, J. Proteome. Res. (2023) 2327-2338. https://doi.org/10.1021/acs.jproteome.3c00093.

Crossref Google Scholar
[46]

M. Igarashi, K. Iwasa, K. Yoshikawa, Feeding regulation by oleoylethanolamide synthesized from dietary oleic acid, Prostaglandins Leukot. Essent. Fatty Acids 165 (2021) 102228. https://doi.org/10.1016/j.plefa.2020.102228.

Crossref Google Scholar
[47]

S.T. Li, X. L. Liao, , F. Y. Meng, et al., Therapeutic role of ursolic acid on ameliorating hepatic steatosis and improving metabolic disorders in high-fat diet-induced non-alcoholic fatty liver disease rats, PLoS ONE 9(1) (2014). https://doi.org/10.1371/journal.pone.0086724.

Crossref Google Scholar
[48]

W.S. Zhang, A. Pan, L. Yang, et al., American ginseng and asian ginseng intervention in diet-induced obese mice: metabolomics reveals distinct metabolic profiles, Am. J. Chin. Med. 47(4) (2019) 787-801. https://doi.org/10.1142/s0192415x19500411.

Crossref Google Scholar
Food Science and Human Wellness
Volume 14 Issue 1,
January 2025
Article number: 9250079
DOI: 10.26599/FSHW.2024.9250079
Cite this article:
Gu T, Lin J, Yang J, et al. Chlorantraniliprole exposure aggravates high-fat diet-induced metabolic disorders in mice by regulating gut microbiota and its metabolites. Food Science and Human Wellness, 2025, 14(1): 9250079. https://doi.org/10.26599/FSHW.2024.9250079
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return

The biomarker metabolites in feces after chlorantraniliprole exposure.

MetabolitesFold changeaP-valuebVIPcState
Note: a Fold change represented the ratio of metabolites relative concentration in M group to Control group. b P-value from t-test. c VIP represented the variable importance in projection based on OPLS-DA model. Differential metabolites screening criteria: VIP ≥ 1, fold change > 1.2 or < 0.83, and P-value < 0.05.
L-Gglutamic acid2.0254960630.0444621.386523174Up
GABA1.9400970730.0443051.370085658Up
5-Guanidino-2-oxopentanoic acid3.2440893140.0285761.586261531Up
L-(+)-Aspartic acid1.7982075880.0395961.772046896Up
Oxoglutaric acid6.8399586330.0222912.243873163Up
Phenylpyruvic acid3.5429866340.0038532.119969158Up
N-acetyl-L-aspartic acid2.3042522750.03099021.771361254Up
N-(2-Phenylethyl)-β-D-glucopyranuronosylamine2.615278190.0078422.299248011Up
8-Hydroxyquinoline2.0888695980.0037371.984440598Up
Chlorantraniliprole225.05135062.06 × 10–97.177089396Up
Oleoyl ethanolamide0.6044210190.0362551.448685018Down
Ursolic acid0.4720705820.0080631.539784264Down
6-Ketoprostaglandin f1α0.7142257180.0477621.082582669Down
11-Dehydro thromboxane B20.5848099210.008391.46721945Down