Multi-omics phenotyping of the gut-liver axis reveals metabolic perturbations from a low-dose pesticide mixture in rats

Health effects of pesticides are not always accurately detected using the current battery of regulatory toxicity tests. We compared standard histopathology and serum biochemistry measures and multi-omics analyses in a subchronic toxicity test of a mixture of six pesticides frequently detected in foodstuffs (azoxystrobin, boscalid, chlorpyrifos, glyphosate, imidacloprid and thiabendazole) in Sprague-Dawley rats. Analysis of water and feed consumption, body weight, histopathology and serum biochemistry showed little effect. Contrastingly, serum and caecum metabolomics revealed that nicotinamide and tryptophan metabolism were affected, which suggested activation of an oxidative stress response. This was not reflected by gut microbial community composition changes evaluated by shotgun metagenomics. Transcriptomics of the liver showed that 257 genes had their expression changed. Gene functions affected included the regulation of response to steroid hormones and the activation of stress response pathways. Genome-wide DNA methylation analysis of the same liver samples showed that 4,255 CpG sites were differentially methylated. Overall, we demonstrated that in-depth molecular profiling in laboratory animals exposed to low concentrations of pesticides allows the detection of metabolic perturbations that would remain undetected by standard regulatory biochemical measures and which could thus improve the predictability of health risks from exposure to chemical pollutants.

Comparison of the course of biomarker changes and kidney injury in a rat model of drug-induced acute kidney injury

OBJECTIVE

To aid in evaluating the performance of biomarkers, we measured kidney injury biomarkers in rat models of drug-induced acute kidney injury.

METHODS AND RESULTS

Rats were treated with site-specific nephrotoxins, puromycin, gentamicin, cisplatin, or 2-bromoethylamine. Fifteen biomarkers (β-2-microglobulin, calbindin, clusterin, cystatin-C, KIM-1, GST-α, GST-μ, NGAL, osteopontin, EGF, TIMP-1, VEGF, albumin, RPA-1, and urinary total protein) were examined in comparison with BUN, serum creatinine, and NAG. Some biomarkers, which were different depending in each nephrotoxin, showed ability to detect the prodromal stage of drug-induced kidney injury. Characteristic changing patterns of biomarkers were also found depending on the specific lesion site in the kidney.

CONCLUSION

These data suggested that establishment of a suitable biomarker panel would facilitate detection of site-specific kidney injury with high sensitivity.