Unraveling biomarkers of exposure for tenuazonic acid through urinary metabolomics

Abstract

Mycotoxins are secondary metabolites produced by fungi such as Aspergillus, Alternaria, and Penicillium, affecting nearly 80% of global food crops. Tenuazonic acid (TeA) is the major mycotoxin produced by Alternaria alternata, a prevalent pathogen affecting plants, fruits, and vegetables. TeA is notably prevalent in European diets, however, TeA biomarkers of exposure and metabolites remain unknown. This research aims to bridge this knowledge-gap by gaining insights about human TeA exposure and metabolization. Nine subjects were divided into two groups. The first group received a single bolus of TeA at the Threshold of Toxicological Concern (TTC) to investigate the presence of TeA urinary biomarkers, while the second group served as a control. Sixty-nine urinary samples were prepared and analyzed using UPLC-Xevo TQ-XS for TeA quantification and UPLC-Orbitrap Exploris for polar metabolome acquisition. TeA was rapidly excreted during the first 13 h and the fraction extracted was 0.39 ± 0.22. The polar metabolome compounds effectively discriminating the two groups were filtered using Orthogonal Partial Least Squares-Discriminant Analysis and subsequently annotated (n = 122) at confidence level 4. Finally, the urinary metabolome was compared to in silico predicted TeA metabolites. Nine metabolites, including oxidized, N-alkylated, desaturated, glucuronidated, and sulfonated forms of TeA were detected.

Keywords: Machine learning; Metabolomics; Mycotoxin; Risk assessment; Tenuazonic acid; Toxicokinetic.

Graphical abstract

Fig. 1

Schematic illustration of the human intervention trial for the investigation of tenuazonic acid (TeA) metabolization in urine..

Fig. 2

Fraction excreted of TeA in urine after ingestion of tenuazonic acid at 1500 ng/kg body weight, during the 48 h of urine collection. The profiles correspond to four volunteers identified by the identification numbers 01, 03, 06 and 09, reported in the legend.

Fig. 3

Principal component analysis (PCA) scores plot built on the original HESI- dataset before the application of the chemometric filtering for the first principal component PC1 against the second (A) and third (B) principal components. PCA scores plot built on the original HESI + for the first principal component PC1 against the second (C) and third (D) principal components. HESI + dataset before the application of the chemometric filtering.

Fig. 4

Score distance (SD) and orthogonal distance (OD) values calculated for each sample on the OPLS-DA models built on the HESI – (A) and HESI + (B) datasets. No sample has values above the threshold for both OD and SD. Therefore, no sample is considered an outlier.

Fig. 5

Permutation test performed on the OPLS-DA model built on the HESI- (A) and HESI+ (B) datasets with 20 iterations. The grey points represent the R2Y values obtained for each permutation. Analogously, the black points represent the Q2Y values. The grey and black lines set the values of R2Y and Q2Y, respectively, of the model trained with the true labels.

Fig. 6

Volcano plots obtained from the HESI- (A) and HESI+ (B) datasets. The down-regulated significant features are coloured in red, while the up-regulated features are coloured in blue, the features not-significantly influenced by the exposure of TeA are coloured in green. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7

OPLS-DA (A) scores and (B) loadings plots relative to the first predictive components and its first orthogonal component built on the HESI- dataset obtained after chemometric filtering. OPLS-DA (C) scores and (C) loadings plots relative to the first predictive components and its first orthogonal component built on the HESI + dataset obtained after chemometric filtering. The samples belonging to the intervention group are coloured in red (TeA), while the ones belonging to the control group are in blue (control). The samples belonging to the intervention group are coloured in red (TeA), while the ones belonging to the control group are in blue (control). Class ellipses represent the 95% confidence regions for class membership.(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8

Confusion matrices obtained for the fitting on the training set (top) and in prediction on the test set (bottom) using the OPLS-DA models cross-validated on the HESI+ (right) and HESI– (left) datasets obtained after chemometric filtering. At the bottom left of each matrix, the respective accuracy is reported.

Fig. 9

Chromatograms of TeA and its 9 metabolites separated from human urine and detected using UPLC-HRMS. The profiles in orange refer to the subjects exposed to tenuazonic acid, while the blue ones to the control group.(For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)