The aim of the present study was to investigate effects of defined mixtures of polycyclic aromatic hydrocarbons (PAHs) and perfluoroalkyl substances (PFASs), at low, environmentally relevant (1× = L), or high (20× = H) doses, on biological responses in Atlantic cod (Gadus morhua). To this end, farmed juvenile cod were exposed at day 0 and day 7 via intraperitoneal (i.p.) injections, in a two-week in vivo experiment. In total, there were 10 groups of fish (n = 21-22): two control groups, four separate exposure groups of PAH and PFAS mixtures (L, H), and four groups combining PAH and PFAS mixtures (L/L, H/L, L/H, H/H). Body burden analyses confirmed a dose-dependent accumulation of PFASs in cod liver and PAH metabolites in bile. The hepatosomatic index (HSI) was significantly reduced for three of the combined PAH/PFAS exposure groups (L-PAH/H-PFAS, H-PAH/L-PFAS, H-PAH/H-PFAS). Analysis of the hepatic proteome identified that pathways related to lipid degradation were significantly affected by PFAS exposure, including upregulation of enzymes in fatty acid degradation pathways, such as fatty acid β-oxidation. The increased abundances of enzymes in lipid catabolic pathways paralleled with decreasing levels of triacylglycerols (TGs) in the H-PFAS exposure group, suggest that PFAS increase lipid catabolism in Atlantic cod. Markers of oxidative stress, including catalase and glutathione S-transferase activities were also induced by PFAS exposure. Only minor and non-significant differences between exposure groups and control were found for cyp1a and acox1 gene expressions, vitellogenin concentrations in plasma, Cyp1a protein synthesis and DNA fragmentation. In summary, our combined proteomics and lipidomics analyses indicate that PFAS may disrupt lipid homeostasis in Atlantic cod.

Background: With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills. Results: Utilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports the mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. The source code of RASflow is available on GitHub: https://github.com/zhxiaokang/RASflow. Conclusions: RASflow is a simple and reliable RNA-Seq analysis workflow covering many use cases.

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.