a pilot mother-child cohort study to assess early-life exposure to mycotoxins: challenges and lessons learned

Funding Information: Funding: The earlyMYCO study was funded by Foundation for Science and Technology (PTDC/DTP-MEDTOX/28762/2017), by CESAM (UIDP/50017/2020+UIDB/50017/2020) and by the project MYTOX-SOUTH, Ghent University Global Minds programme. The funding sources were not involved in study design, in the collection, analysis and interpretation of data, and in the decision to submit the article for publication. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Early-life exposure occurs during gestation through transfer to the fetus and later, during lactation. Recent monitoring data revealed that the Portuguese population is exposed to mycotoxins, including young children. This study aimed to develop a pilot study to assess the earlylife exposure to mycotoxins through a mother–child cohort, and to identify the associated challenges. Participants were recruited during pregnancy (1st trimester) and followed-up in three moments of observation: 2nd trimester of pregnancy (mother), and 1st and 6th month of the child’s life (mother and child), with the collection of biological samples and sociodemographic and food consumption data. The earlyMYCO pilot study enrolled 19 mother–child pairs. The analysis of biological samples from participants revealed the presence of 4 out of 15 and 5 out of 18 mycotoxins’ biomarkers of exposure in urine and breast milk samples, respectively. The main aspects identified as contributors for the successful development of the cohort were the multidisciplinary and dedicated team members in healthcare units, reduced burden of participation, and the availability of healthcare units for the implementation of the fieldwork. Challenges faced, lessons learned, and suggestions were discussed as a contribution for the development of further studies in this area.publishersversionpublishe

Validation of a UPLC-MS/MS Method for Multi-Matrix Biomonitoring of <i>Alternaria</i> Toxins in Humans

Mycotoxins, natural toxins produced by fungi, contaminate nearly 80% of global food crops. Alternaria mycotoxins, including alternariol (AOH), alternariol monomethylether (AME), and tenuazonic acid (TeA), present a health concern due to their prevalence in various plants and fruits. Exposure to these toxins exceeds the threshold of toxicological concern in some European populations, especially infants and toddlers. Despite this, regulatory standards for Alternaria toxins remain absent. The lack of toxicokinetic parameters, reference levels, and sensitive detection methods complicates risk assessment and highlights the necessity for advanced biomonitoring (HBM) techniques. This study addresses these challenges by developing and validating ultra-high performance liquid chromatography method coupled with tandem mass spectrometry to quantify AOH, AME, TeA, and their conjugates in multiple biological matrices. The validated method demonstrates robust linearity, precision, recovery (94–111%), and sensitivity across urine (LOD Alternaria toxins, facilitating the generation of data for regulatory authorities

Associating multiple mycotoxin exposure and health outcomes : current statistical approaches and challenges

Mycotoxin contamination is a global challenge to food safety and population health. A diversity of adverse effects in human health such as organ damage, immunity disorders and carcinogenesis are attributed to acute and chronic exposure to mycotoxins. While there is a high likelihood of mycotoxin co-occurrence in the daily diet, multiple mycotoxin exposures represent a considerable challenge in understanding the accumulative effects of groups of exposures on health outcomes. Nevertheless, previous studies on mycotoxin exposure-health outcome associations have focused on a single or a limited number of exposures. To guide multi-exposure assessment, careful considerations of statistical approaches available are required. In addition, the issue of multicollinearity in high-dimensional settings of multiple exposure analysis underlies the controversy surrounding the reliability and consistency of statistical conclusions about the exposure-health outcome associations. Conventional approaches such as generalised linear regressions (GLR) in conjunction with regularisation methods, including ridge regression, lasso and elastic net, offer some clear advantages in terms of results' interpretation and model selection. However, when highly-correlated variables are observed, these methods have shown a low specificity in variable selection. Principal component analysis (PCA) that has been widely used as a dimensionality reduction technique also has the limitation to identify important predictor variables as this approach may overlook the associations between certain components and health outcomes. Recently, some alternative approaches have been introduced to address the issues of high dimensionality and highly-correlated data in the context of epidemiological and environmental research. Two of the noticeable approaches are weighted quantile sum regression (WQSR) and Bayesian kernel machine regression (BKMR). Combining different methods of inference allows us to interpret the role of certain exposures, their interactions and the combined effects on human health under diverse statistical perspectives, which ultimately facilitate the construction of the toxicological profile of multiple mycotoxins' exposure

Untargeted Metabolomics in Forensic Toxicology: A New Approach for the Detection of Fentanyl Intake in Urine Samples

The misuse of fentanyl, and novel synthetic opioids (NSO) in general, has become a public health emergency, especially in the United States. The detection of NSO is often challenged by the limited diagnostic time frame allowed by urine sampling and the wide range of chemically modified analogues, continuously introduced to the recreational drug market. In this study, an untargeted metabolomics approach was developed to obtain a comprehensive “fingerprint” of any anomalous and specific metabolic pattern potentially related to fentanyl exposure. In recent years, in vitro models of drug metabolism have emerged as important tools to overcome the limited access to positive urine samples and uncertainties related to the substances actually taken, the possible combined drug intake, and the ingested dose. In this study, an in vivo experiment was designed by incubating HepG2 cell lines with either fentanyl or common drugs of abuse, creating a cohort of 96 samples. These samples, together with 81 urine samples including negative controls and positive samples obtained from recent users of either fentanyl or “traditional” drugs, were subjected to untargeted analysis using both UHPLC reverse phase and HILIC chromatography combined with QTOF mass spectrometry. Data independent acquisition was performed by SWATH in order to obtain a comprehensive profile of the urinary metabolome. After extensive processing, the resulting datasets were initially subjected to unsupervised exploration by principal component analysis (PCA), yielding clear separation of the fentanyl positive samples with respect to both controls and samples positive to other drugs. The urine datasets were then systematically investigated by supervised classification models based on soft independent modeling by class analogy (SIMCA) algorithms, with the end goal of identifying fentanyl users. A final single-class SIMCA model based on an RP dataset and five PCs yielded 96% sensitivity and 74% specificity. The distinguishable metabolic patterns produced by fentanyl in comparison to other opioids opens up new perspectives in the interpretation of the biological activity of fentanyl

Unraveling biomarkers of exposure for tenuazonic acid through urinary metabolomics

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 UPLCOrbitrap 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

Control of spasticity in a multiple sclerosis model using central nervous system-excluded CB1 cannabinoid receptor agonists

The purpose of this study was the generation of central nervous system (CNS)-excluded cannabinoid receptor agonists to test the hypothesis that inhibition of spasticity, due to CNS autoimmunity, could be controlled by affecting neurotransmission within the periphery. Procedures included identification of chemicals and modeling to predict the mode of exclusion; induction and control of spasticity in the ABH mouse model of multiple sclerosis; conditional deletion of CB1 receptor in peripheral nerves; side effect profiling to demonstrate the mechanism of CNSexclusion via drug pumps; genome-wide association study in N2(129×ABH) backcross to map polymorphic cannabinoid drug pump; and sequencing and detection of cannabinoid drug-pump activity in human brain endothelial cell lines. Three drugs (CT3, SAB378 and SAD448) were identified that control spasticity via action on the peripheral nerve CB1 receptor. These were peripherally restricted via drug pumps that limit the CNS side effects (hypothermia) of cannabinoids to increase the therapeutic window. A cannabinoid drug pump is polymorphic and functionally lacking in many laboratory (C57BL/6, 129, CD-1) mice used for transgenesis, pharmacology, and toxicology studies. This phenotype was mapped and controlled by 1-3 genetic loci. ABCC1 within a cluster showing linkage is a cannabinoid CNS-drug pump. Global and conditional CB1 receptor-knockout mice were used as controls. In summary, CNS-excluded CB1 receptor agonists are a novel class of therapeutic agent for spasticity.SCOPUS: ar.jinfo:eu-repo/semantics/publishe