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Surveillance of illegal use of steroids hormones in cattle breeding is a key issue to preserve human health. To this purpose, an integrated approach has been developed for the analysis of plasma and urine from calves treated orally with a single dose of a combination of the androgenic steroids boldenone and boldione. A quantitative estimation of steroid hormones was obtained by LC-APCI-Q-MS/MS analysis of plasma and urine samples obtained at various times up to 36 and 24 h after treatment, respectively. These experiments demonstrated that boldione was never found, while boldenone alpha- and beta-epimers were detected in plasma and urine only within 2 and 24 h after drug administration, respectively. Parallel proteomic analysis of plasma samples was obtained by combined 2-DE, MALDI-TOF-MS and muLC-ESI-IT-MS/MS procedures. A specific protein, poorly represented in normal plasma samples collected before treatment, was found upregulated even 36 h after hormone treatment. Extensive mass mapping experiments proved this component as an N-terminal truncated form of apolipoprotein A1 (ApoA1), a protein involved in cholesterol transport. The expression profile of ApoA1 analysed by Western blot analysis confirmed a significant and time dependent increase of this ApoA1 fragment. Then, provided that further experiments performed with a growth-promoting schedule will confirm these preliminary findings, truncated ApoA1 may be proposed as a candidate biomarker for steroid boldenone and possibly other anabolic androgens misuse in cattle veal calves, when no traces of hormones are detectable in plasma or urine.

In this work the feasibility of near infrared spectroscopy was evaluated combined with chemometric approaches, as a tool for the botanical origin prediction of 119 honey samples. Four varieties related to polyfloral, acacia, chestnut, and linden were first characterized by their physical-chemical parameters and then analyzed in triplicate using a near infrared spectrophotometer equipped with an optical path gold reflector. Three different classifiers were built on distinct multivariate and machine learning approaches for honey botanical classification. A partial least squares discriminant analysis was used as a first approach to build a predictive model for honey classification. Spectra pretreatments named autoscale, standard normal variate, detrending, first derivative, and smoothing were applied for the reduction of scattering related to the presence of particle size, like glucose crystals. The values of the descriptive statistics of the partial least squares discriminant analysis model allowed a sufficient floral group prediction for the acacia and polyfloral honeys but not in the cases of chestnut and linden. The second classifier, based on a support vector machine, allowed a better classification of acacia and polyfloral and also achieved the classification of chestnut. The linden samples instead remained unclassified. A further investigation, aimed to improve the botanical discrimination, exploited a feature selection algorithm named Boruta, which assigned a pool of 39 informative averaged near infrared spectral variables on which a canonical discriminant analysis was assessed. The canonical discriminant analysis accounted a better separation of samples according to the botanical origin than the partial least squares discriminant analysis. The approach used has permitted to achieve a complete authentication of the acacia honeys but not a precise segregation of polyfloral ones. The comparison between the variables important in projection and the Boruta pool showed that the informative wavelengths are partially shared especially in the middle and far band of the near infrared spectral range.

Cytochrome P450 3A is the most important CYP subfamily in humans, and CYP3A4/CYP3A5 genetic variants contribute to inter-individual variability in drug metabolism. However, no information is available for bovine CYP3A (bCYP3A). Here we described bCYP3A missense single nucleotide variants (SNVs) and evaluated their functional effects. CYP3A28, CYP3A38 and CYP3A48 missense SNVs were identified in 300 bulls of Piedmontese breed through targeted sequencing. Wild-type and mutant bCYP3A cDNAs were cloned and expressed in V79 cells. CYP3A-dependent oxidative metabolism of testosterone (TST) and nifedipine (NIF) was assessed by LC-MS/MS. Finally, SNVs functional impact on TST hydroxylation was measured ex vivo in liver microsomes from individually genotyped animals. Thirteen missense SNVs were identified and validated. Five variants showed differences in CYP3A catalytic activity: three CYP3A28 SNVs reduced TST 6β-hydroxylation; one CYP3A38 variant increased TST 16β-hydroxylation, while a CYP3A48 SNV showed enhanced NIF oxidation. Individuals homozygous for rs384467435 SNV showed a reduced TST 6β-hydroxylation. Molecular modelling showed that most of SNVs were distal to CYP3A active site, suggesting indirect effects on the catalytic activity. Collectively, these findings demonstrate the importance of pharmacogenetics studies in veterinary species and suggest bCYP3A genotype variation might affect the fate of xenobiotics in food-producing species such as cattle.

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Toxic pyrrolizidine alkaloids (PAs) and their N-oxides (PANOs) can be present in bee pollen depending on the plants visited by bees. A liquid chromatography-mass spectrometry (LC-MS/MS) method was developed and validated to monitor 17 PAs/PANOs in 44 bee pollens. The CIE-L*a*b* colour coordinates with the specular component either included or excluded were recorded in pellets and ground aliquots. Lightness (L*) and yellowness (b*) of ground bee pollen were significantly correlated to PAs/PANOs content. The L* and b* cut-offs sorted by a receiver operating characteristic analysis to predict PAs/PANOs presence showed a significant increase in the relative risk to detect amounts higher than 84 μg kg À1 . Two supervised canonical discriminant analyses confirmed that pollen without PAs could be distinguished from those containing PAs/PANOs. The data suggest that instrumental colour coupled with supervised models could be used as a screening test for PAs/PANOs in bee pollen, before the confirmatory LC-MS/MS analysis.

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