Changes of Milk Metabolomic Profiles Resulting from a Mycotoxins-Contaminated Corn Silage Intake by Dairy Cows

The Potential of Multi-Screening Methods and Omics Technologies to Detect Both Regulated and Emerging Mycotoxins in Different Matrices

Mycotoxins are well-known secondary metabolites produced by several fungi that grow and occur in different crops during both pre-harvest and post-harvest conditions. The contamination and occurrence of mycotoxins currently represent some of the major issues in the entire agri-food system. The quantification of mycotoxins in different feeds and foodstuffs is extremely difficult because of the low concentration ranges; therefore, both sample collection and preparation are essential to providing accurate detection and reliable quantification. Currently, several analytical methods are available for the detection of mycotoxins in both feed and food products, and liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) represents the most reliable instrumental approach. In particular, the fast development of high-throughput methods has made it possible to screen and analyze, in the same analytical run and with high accuracy, multiple mycotoxins, such as those regulated, masked, or modified, and emerging ones. Therefore, the aim of this review is to provide an overview of the state of the art of mycotoxins occurrence, health-related concerns, and analyses, discussing the need to perform multi-screening approaches combined with omics technologies to simultaneously analyze several mycotoxins in different feed and food matrices. This approach is expected to provide more comprehensive information about the profile and distribution of emerging mycotoxins, thus enhancing the understanding of their co-occurrence and impact on the entire production chain.

New Insights Into Short-term Water Stress Tolerance Through Transcriptomic and Metabolomic Analyses on Pepper Roots

In the current climate change scenario, water stress is a serious threat to limit crop growth and yields. It is necessary to develop tolerant plants that cope with water stress and, for this purpose, tolerance mechanisms should be studied. NIBER® is a proven water stress- and salt-tolerant pepper hybrid rootstock (Gisbert-Mullor et al., 2020; López-Serrano et al., 2020), but tolerance mechanisms remain unclear. In this experiment, NIBER® and A10 (a sensitive pepper accession (Penella et al., 2014)) response to short-term water stress at 5 h and 24 h was studied in terms of gene expression and metabolites content in roots. GO terms and gene expression analyses evidenced constitutive differences in the transcriptomic profile of NIBER® and A10, associated with detoxification systems of reactive oxygen species (ROS). Upon water stress, transcription factors like DREBs and MYC are upregulated and the levels of auxins, abscisic acid and jasmonic acid are increased in NIBER®. NIBER® tolerance mechanisms involve an increase in osmoprotectant sugars (i.e., trehalose, raffinose) and in antioxidants (spermidine), but lower contents of oxidized glutathione compared to A10, which indicates less oxidative damage. Moreover, the gene expression for aquaporins and chaperones is enhanced. These results show the main NIBER® strategies to overcome water stress.

Milk metabolome reveals pyrimidine and its degradation products as the discriminant markers of different corn silage-based nutritional strategies

The purpose of this study was to evaluate the effect of 6 different feeding systems (based on corn silage as the main ingredient) on the chemical composition of milk and to highlight the potential of untargeted metabolomics to find discriminant marker compounds of different nutritional strategies. Interestingly, the multivariate statistical analysis discriminated milk samples mainly according to the high-moisture ear corn (HMC) included in the diet formulation. Overall, the most discriminant compounds, identified as a function of the HMC, belonged to AA (10 compounds), peptides (71 compounds), pyrimidines (38 compounds), purines (15 compounds), and pyridines (14 compounds). The discriminant milk metabolites were found to significantly explain the metabolic pathways of pyrimidines and vitamin B₆. Interestingly, pathway analyses revealed that the inclusion of HMC in the diet formulation strongly affected the pyrimidine metabolism in milk, determining a significant up-accumulation of pyrimidine degradation products, such as 3-ureidopropionic acid, 3-ureidoisobutyric acid, and 3-aminoisobutyric acid. Also, some pyrimidine intermediates (such as l-aspartic acid, N-carbamoyl-l-aspartic acid, and orotic acid) were found to possess a high discrimination degree. Additionally, our findings suggested that the inclusion of alfalfa silage in the diet formulation was potentially correlated with the vitamin B₆ metabolism in milk, being 4-pyridoxic acid (a pyridoxal phosphate degradation product) the most significant and up-accumulated compound. Taken together, the accumulation trends of different marker compounds revealed that both pyrimidine intermediates and degradation products are potential marker compounds of HMC-based diets, likely involving a complex metabolism of microbial nitrogen based on total splanchnic fluxes from the rumen to mammary gland in dairy cows. Also, our findings highlight the potential of untargeted metabolomics in both foodomics and foodomics-based studies involving dairy products.

Occurrence of Polyphenols, Isoflavonoids, and Their Metabolites in Milk Samples from Different Cow Feeding Regimens

In this work, milk samples collected in a cohort of intensive dairy farms of the Po Valley (Italy) were screened for their (poly)-phenolic profile to check the occurrence of phenolic metabolites of biological interest. The selected dairy farms were previously classified on the basis of their cow feeding system, considering the utilization of corn silage as the main ingredient of the rations. Overall, ultra-high-pressure liquid chromatography coupled with mass spectrometry using an Orbitrap analyzer, followed by unsupervised and supervised statistics, allowed identifying clear different phenolic distributions in the milk samples. Accordingly, a great variability in the phenolic profiles of the different milk samples was observed, with two main phenolic clusters outlined by the unsupervised hierarchical clustering approach and not fully correlated to the nutritional strategy considered. The variables’ importance in the projection approach allowed selecting the most important metabolites, resulting in samples’ discrimination. Among the most discriminative compounds, we found phenolic metabolites (such as hippuric acid and 4-hydroxyhippuric acid), followed by lignans (such as enterolactone) and isoflavonoids (such as equol and O-desmethylangolensin). Taken together, our findings suggested that both the feeding systems and the ability of dairy cows to process parent phenolic compounds were the main factors providing the final (poly)-phenolic profile of the milk samples. Future targeted and ad hoc studies appear of great interest to evaluate the potential biological effects of these compounds on cow health.

A Preliminary Study to Classify Corn Silage for High or Low Mycotoxin Contamination by Using near Infrared Spectroscopy

Mycotoxins should be monitored in order to properly evaluate corn silage safety quality. In the present study, corn silage samples (n = 115) were collected in a survey, characterized for concentrations of mycotoxins, and scanned by a NIR spectrometer. Random Forest classification models for NIR calibration were developed by applying different cut-offs to classify samples for concentration (i.e., μg/kg dry matter) or count (i.e., n) of (i) total detectable mycotoxins; (ii) regulated and emerging Fusarium toxins; (iii) emerging Fusarium toxins; (iv) Fumonisins and their metabolites; and (v) Penicillium toxins. An over- and under-sampling re-balancing technique was applied and performed 100 times. The best predictive model for total sum and count (i.e., accuracy mean ± standard deviation) was obtained by applying cut-offs of 10,000 µg/kg DM (i.e., 96.0 ± 2.7%) or 34 (i.e., 97.1 ± 1.8%), respectively. Regulated and emerging Fusarium mycotoxins achieved accuracies slightly less than 90%. For the Penicillium mycotoxin contamination category, an accuracy of 95.1 ± 2.8% was obtained by using a cut-off limit of 350 µg/kg DM as a total sum or 98.6 ± 1.3% for a cut-off limit of five as mycotoxin count. In conclusion, this work was a preliminary study to discriminate corn silage for high or low mycotoxin contamination by using NIR spectroscopy.

Screening of Regulated and Emerging Mycotoxins in Bulk Milk Samples by High-Resolution Mass Spectrometry

In this work, a retrospective screening based on ultra-high-performance liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) based on Orbitrap-Q-Exactive Focus™ was used to check the occurrence of regulated and emerging mycotoxins in bulk milk samples. Milk samples were collected from dairy farms in which corn silage was the main ingredient of the feeding system. The 45 bulk milk samples were previously analyzed for a detailed untargeted metabolomic profiling and classified into five clusters according to the corn silage contamination profile, namely: (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites. Multivariate statistics based on both unsupervised and supervised analyses were used to evaluate the significant fold-change variations of the main groups of mycotoxins detected when comparing milk samples from clusters 3, 4, and 5 (high contamination levels of the corn silages) with cluster 1 and 2 (low contamination levels of the corn silages). Overall, 14 compounds showed a significant prediction ability, with antibiotic Y (VIP score = 2.579), bikaverin (VIP score = 1.975) and fumonisin B2 (VIP score = 1.846) being the best markers. The k-means clustering combined with supervised statistics showed two discriminant groups of milk samples, thus revealing a hierarchically higher impact of the whole feeding system (rather than the only corn silages) together with other factors of variability on the final mycotoxin contamination profile. Among the discriminant metabolites we found some Fusarium mycotoxins, together with the tetrapeptide tentoxin (an Alternaria toxin), the α-zearalenol (a catabolite of zearalenone), mycophenolic acid and apicidin. These preliminary findings provide new insights into the potential role of UHPLC-HRMS to evaluate the contamination profile and the safety of raw milk to produce hard cheese.