Thyroid function and immune status in perch (Perca fluviatilis) from lakes contaminated with PFASs or PCBs

The environment contains a multitude of man-made chemicals, some of which can act as endocrine disruptors (EDCs), while others can be immunotoxic. We evaluated thyroid disruption and immunotoxic effects in wild female perch (Perca fluviatilis) collected from two contaminated areas in Sweden; one site contaminated with perand polyfluoroalkyl substances (PFASs) and two sites contaminated with polychlorinated biphenyls (PCBs), with one reference site included for each area. The hepatic mRNA expression of thyroid receptors alpha and beta, and the thyroid hormone metabolising iodothyronine deiodinases (dio1, dio2 and dio3) were measured using real-time PCR, while the levels of thyroid hormone T3 in plasma was analysed using a radioimmunoassay. In addition, lymphocytes, granulocytes, and thrombocytes were counted microscopically. Our results showed lower levels of T3 as well as lower amounts of lymphocytes and granulocytes in perch collected from the PFAS-contaminated site compared to reference sites. In addition, expressions of mRNA coding for thyroid hormone metabolising enzymes (dio2 and dio3) and thyroid receptor alpha (thra) were significantly different in these fish compared to their reference site. For perch collected at the two PCB-contaminated sites, there were no significant differences in T3 levels or in expression levels of the thyroid-related genes, compared to the reference fish. Fish from one of the PCB-contaminated sites had higher levels of thrombocytes compared with both the second PCB lake and their reference lake; hence PCBs are unlikely to be the cause of this effect. The current study suggests that lifelong exposure to PFASs could affect both the thyroid hormone status and immune defence of perch in the wild

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions