Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

Phosphodiesterase type 5 inhibitors enhance chemotherapy in preclinical models of esophageal adenocarcinoma by targeting cancer-associated fibroblasts.

The chemotherapy resistance of esophageal adenocarcinomas (EACs) is underpinned by cancer cell extrinsic mechanisms of the tumor microenvironment (TME). We demonstrate that, by targeting the tumor-promoting functions of the predominant TME cell type, cancer-associated fibroblasts (CAFs) with phosphodiesterase type 5 inhibitors (PDE5i), we can enhance the efficacy of standard-of-care chemotherapy. In ex vivo conditions, PDE5i prevent the transdifferentiation of normal fibroblasts to CAF and abolish the tumor-promoting function of established EAC CAFs. Using shotgun proteomics and single-cell RNA-seq, we reveal PDE5i-specific regulation of pathways related to fibroblast activation and tumor promotion. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patient and in vivo PDX-based model systems. These findings demonstrate that CAFs drive chemotherapy resistance in EACs and can be targeted by repurposing PDE5i, a safe and well-tolerated class of drug administered to millions of patients world-wide to treat erectile dysfunction.

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.

The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.

Applying and improving AlphaFold at CASP14.

We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the "human" category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is entirely different to the one entered in CASP13. It used a novel end-to-end deep neural network trained to produce protein structures from amino acid sequence, multiple sequence alignments, and homologous proteins. In the assessors' ranking by summed z scores (>2.0), AlphaFold scored 244.0 compared to 90.8 by the next best group. The predictions made by AlphaFold had a median domain GDT_TS of 92.4; this is the first time that this level of average accuracy has been achieved during CASP, especially on the more difficult Free Modeling targets, and represents a significant improvement in the state of the art in protein structure prediction. We reported how AlphaFold was run as a human team during CASP14 and improved such that it now achieves an equivalent level of performance without intervention, opening the door to highly accurate large-scale structure prediction.

Highly accurate protein structure prediction with AlphaFold.

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1-4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'8-has been an important open research problem for more than 50 years9. Despite recent progress10-14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.

Highly accurate protein structure prediction for the human proteome.

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

Transcriptome network data in larval zebrafish (Danio rerio) following exposure to the phenylpyrazole fipronil.

Fipronil is a phenylpyrazole pesticide that is used in both residential and agricultural applications. Fipronil is detected in run-off and water systems that are near areas in which the pesticide has been applied. The pesticide acts to antagonize gamma aminobutyric acid receptors, leading to over-excitation in the central nervous system. Fipronil has relatively high toxicity to fish, but the mechanisms underlying the toxicity are not well understood in embryonic stages. Zebrafish embryos were exposed to a single concentration of fipronil for 48 h at ∼3-4 h-post-fertilization. Following a 7-day depuration phase, transcriptome and behavioral analyses were conducted. Transcriptomics identified neural processes as those differentially expressed with different doses of fipronil (0.2 µg, 200 µg and 2 mg fipronil/L). Gene networks associated with astrocyte differentiation, myelination, neural tube development, brain stem response, innervation, nerve regeneration, astrocyte differentiation, among other pathways were altered with exposure. In addition, miRNA-related events are disrupted by fipronil exposure and genes associated with primary or pri-miRNA processing were increased in larval fish exposed to the pesticide. These data present putative mechanisms associated with neurological impacts at later ages of zebrafish. This is important because it is not clear how early exposure to pesticides like fipronil affect central nervous system function and organisms later in life.

Residual molecular and behavioral effects of the phenylpyrazole pesticide fipronil in larval zebrafish (Danio rerio) following a pulse embryonic exposure.

Pesticides are typically applied to crops as acute applications, and residual effects of such intermittent exposures are not often characterized in developing fish. Fipronil is an agricultural pesticide that inhibits γ-amino-butyric acid (GABA) gated chloride channels. In this study, zebrafish (Danio rerio) embryos were exposed for 48 h (starting at ~3 h post fertilization, hpf) to various concentrations of fipronil (0.02 µg/L up to 4000 µg/L). Following this acute exposure, a subset of fish was transferred to clean water for a 7-day depuration phase. We hypothesized that a pulse exposure to fipronil during critical periods of central nervous system development would adversely affect fish later in life. After a 48 hour pulse exposure, survival was reduced in embryos exposed to 2 µg fipronil/L or greater. However, there was no further mortality during the depuration phase, nor were there changes in body length nor notochord length in larvae 9 dpf (days post-fertilization) compared to controls. Additional experiments were carried out at higher concentrations over 96 h (up to 4 dpf) to also elucidate developmental effects and teratogenicity of fipronil (43.7 µg/L up to 4370 µg/L). Fipronil at these higher concentrations significantly impacted the development of zebrafish, and the following morphometric and teratogenic effects were observed in 4 dpf fish; reduced body length, yolk sac and pericardial edema, reduced midbrain length, reduced optic and otic diameter, and truncation of the lower jaw. In depurated fish, we hypothesized that there would exist residual effects of exposure at the molecular level. Transcriptome profiling was therefore conducted on 9 dpf depurated larvae exposed initially for 48 h to one dose of either 0.2 µg/L, 200 µg/L or 2000 µg/L fipronil. The expression of gene networks associated with glycogen and omega-3-fatty acid metabolism were decreased in larvae exposed to each of the three concentrations of fipronil, suggesting metabolic disruption. Moreover, transcriptomics revealed that fipronil suppressed gene networks related to light-dark adaptation, photoperiod sensing, and circadian rhythm. Based on these data, we tested fish for altered behavioral responses in a Light-Dark preference test. Larvae exposed to >200 µg fipronil/L as embryos showed fewer number of visits (20-30% less) to the dark zone compared to controls. Larvae also spent a lower amount of time in the dark zone compared to controls, suggesting that fipronil strengthened dark avoidance behavior which is indicative of anxiety. This study demonstrates that a short pulse exposure to fipronil can affect transcriptome networks for metabolism, circadian rhythm, and response to light in fish after depuration, and these molecular responses are hypothesized to be related to aberrant behavioral effects observed in the light-dark preference test.

Branched-chain ketoacid overload inhibits insulin action in the muscle.

Branched-chain α-keto acids (BCKAs) are catabolites of branched-chain amino acids (BCAAs). Intracellular BCKAs are cleared by branched-chain ketoacid dehydrogenase (BCKDH), which is sensitive to inhibitory phosphorylation by BCKD kinase (BCKDK). Accumulation of BCKAs is an indicator of defective BCAA catabolism and has been correlated with glucose intolerance and cardiac dysfunction. However, it is unclear whether BCKAs directly alter insulin signaling and function in the skeletal and cardiac muscle cell. Furthermore, the role of excess fatty acids (FAs) in perturbing BCAA catabolism and BCKA availability merits investigation. By using immunoblotting and ultra-performance liquid chromatography MS/MS to analyze the hearts of fasted mice, we observed decreased BCAA-catabolizing enzyme expression and increased circulating BCKAs, but not BCAAs. In mice subjected to diet-induced obesity (DIO), we observed similar increases in circulating BCKAs with concomitant changes in BCAA-catabolizing enzyme expression only in the skeletal muscle. Effects of DIO were recapitulated by simulating lipotoxicity in skeletal muscle cells treated with saturated FA, palmitate. Exposure of muscle cells to high concentrations of BCKAs resulted in inhibition of insulin-induced AKT phosphorylation, decreased glucose uptake, and mitochondrial oxygen consumption. Altering intracellular clearance of BCKAs by genetic modulation of BCKDK and BCKDHA expression showed similar effects on AKT phosphorylation. BCKAs increased protein translation and mTORC1 activation. Pretreating cells with mTORC1 inhibitor rapamycin restored BCKA's effect on insulin-induced AKT phosphorylation. This study provides evidence for FA-mediated regulation of BCAA-catabolizing enzymes and BCKA content and highlights the biological role of BCKAs in regulating muscle insulin signaling and function.

Hepatic proteome network data in zebrafish (Danio rerio) liver following dieldrin exposure.

Dieldrin is an environmental contaminant that adversely affects aquatic organisms. The data presented in this study are proteomic data collected in liver of zebrafish that were exposed to the pesticide in a dietary exposure. For label free proteomics, data were collected with a quadrupole Time-of-Flight mass spectrometer and for iTRAQ proteomics, data were acquired using a hybrid quadrupole Orbitrap (Q Exactive) MS system. Using formic acid digestion and label free proteomics, 2,061 proteins were identified, and among those, 103 were differentially abundant (p < 0.05 in at least one dose). In addition, iTRAQ proteomics identified 722 proteins in the liver of zebrafish following dieldrin treatment. The label-free approach identified 21 proteins that followed a dose dependent response. Of the differentially abundant proteins identified by iTRAQ, there were 26 unique expression patterns for proteins based on the three doses of dieldrin. Proteins were queried for disease networks to learn more about adverse effects in the liver following dieldrin exposure. Differentially abundant proteins were related to metabolic disease, steatohepatitis and lipid metabolism disorders, drug-induced liver injury, neoplasms, tissue degeneration and liver metastasis. The proteomics data described here is associated with a research article, "Label-free and iTRAQ proteomics analysis in the liver of zebrafish (Danio rerio) following a dietary exposure to the organochlorine pesticide dieldrin" (Simmons et al. 2019). This investigation reveals new biomarkers of toxicity and will be of interest to those studying aquatic toxicology and pesticides.

Label-free and iTRAQ proteomics analysis in the liver of zebrafish (Danio rerio) following dietary exposure to the organochlorine pesticide dieldrin.

The organochlorine dieldrin (DLD) bioaccumulates in lipid-rich tissues and is associated with immunosuppression, altered metabolism, and cancer. The objective of this study was to determine the effect of DLD on the hepatic proteome in zebrafish following dietary treatment as the liver is central to metabolism. Females were fed a control dose or one of three doses of DLD-contaminated food pellets over 21 days. Both label-free and iTRAQ proteomics were conducted as two complementary methods to expand coverage of the proteome. Label-free proteomics quantified 1563 proteins: 6 proteins showed a linear dose-response with DLD. iTRAQ quantified >3500 proteins; 5 proteins were decreased and 34 proteins were increased in abundance within the liver with all three doses. Overall, DLD reduced the abundance of proteins associated with glucose and cholesterol metabolism, lipid oxidation, liver function, and immune-related processes. Few proteins were identified by both methods as being altered (~1%), suggesting that each method detected different subsets of proteins. Protein responses in the liver were largely dependent on dose, however proteins related to liver and organ function, centrosome separation, glucose/energy metabolism, and immune-related pathways were confirmed by each independent technique and were suppressed with DLD exposure. This study identifies proteomic responses that are associated with organochlorine-induced hepatotoxicity. BIOLOGICAL SIGNIFICANCE: Environmental contaminants cause hepatotoxicity because the liver is the major organ for detoxification. The legacy pesticide dieldrin significantly bioaccumulates in tissues, and can affect molecular processes that can lead to liver pathology. LC MS/MS proteomics identified protein networks related to tumors, energy homeostasis, and chromosomal separation as those affected by dietary exposure to dieldrin. We applied two orthogonal mass spectrometry-based methods to more completely survey the liver proteome, strengthening data interpretation. These data improve understanding as to the effects of organochlorine pesticide toxicity in the liver and the study identifies proteome networks that can contribute to adverse outcome pathways for pesticide exposure.

Author Correction: Authentication and characterisation of a new oesophageal adenocarcinoma cell line: MFD-1.

A correction has been published and is appended to both the HTML and PDF versions of this paper. The error has not been fixed in the paper.

Autotaxin-LPA signaling contributes to obesity-induced insulin resistance in muscle and impairs mitochondrial metabolism.

Autotaxin (ATX) is an adipokine that generates the bioactive lipid, lysophosphatidic acid (LPA). ATX-LPA signaling has been implicated in diet-induced obesity and systemic insulin resistance. However, it remains unclear whether the ATX-LPA pathway influences insulin function and energy metabolism in target tissues, particularly skeletal muscle, the major site of insulin-stimulated glucose disposal. The objective of this study was to test whether the ATX-LPA pathway impacts tissue insulin signaling and mitochondrial metabolism in skeletal muscle during obesity. Male mice with heterozygous ATX deficiency (ATX+/-) were protected from obesity, systemic insulin resistance, and cardiomyocyte dysfunction following high-fat high-sucrose (HFHS) feeding. HFHS-fed ATX+/- mice also had improved insulin-stimulated AKT phosphorylation in white adipose tissue, liver, heart, and skeletal muscle. Preserved insulin-stimulated glucose transport in muscle from HFHS-fed ATX+/- mice was associated with improved mitochondrial pyruvate oxidation in the absence of changes in fat oxidation and ectopic lipid accumulation. Similarly, incubation with LPA decreased insulin-stimulated AKT phosphorylation and mitochondrial energy metabolism in C2C12 myotubes at baseline and following palmitate-induced insulin resistance. Taken together, our results suggest that the ATX-LPA pathway contributes to obesity-induced insulin resistance in metabolically relevant tissues. Our data also suggest that LPA directly impairs skeletal muscle insulin signaling and mitochondrial function.

Validation of optimal reference genes for quantitative real time PCR in muscle and adipose tissue for obesity and diabetes research.

The global incidence of obesity has led to an increasing need for understanding the molecular mechanisms that drive this epidemic and its comorbidities. Quantitative real-time RT-PCR (RT-qPCR) is the most reliable and widely used method for gene expression analysis. The selection of suitable reference genes (RGs) is critical for obtaining accurate gene expression information. The current study aimed to identify optimal RGs to perform quantitative transcriptomic analysis based on RT-qPCR for obesity and diabetes research, employing in vitro and mouse models, and human tissue samples. Using the ReFinder program we evaluated the stability of a total of 15 RGs. The impact of choosing the most suitable RGs versus less suitable RGs on RT-qPCR results was assessed. Optimal RGs differed between tissue and cell type, species, and experimental conditions. By employing different sets of RGs to normalize the mRNA expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α), we show that sub-optimal RGs can markedly alter the PGC1α gene expression profile. Our study demonstrates the importance of validating RGs prior to normalizing transcriptional expression levels of target genes and identifies optimal RG pairs for reliable RT-qPCR normalization in cells and in human and murine muscle and adipose tissue for obesity/diabetes research.

The pesticide dieldrin disrupts proteins related to oxidative respiration and mitochondrial stress in the central nervous system.

Quantitative proteins analysis was carried out in the hypothalamus of zebrafish following dietary exposure to the legacy pesticide dieldrin. Data were collected using iTRAQ labeling methodology and data were acquired using a hybrid quadrupole Orbitrap (Q Exactive) MS system (Thermo Fisher Scientific, Bremen, Germany). There were 3941 proteins identified in the hypothalamus of zebrafish, and these proteins comprised 23 unique expression patterns for proteins based on the three doses of dieldrin. There were 226 proteins that were regulated in one or more doses of dieldrin and 3715 proteins that were not affected. Thus, 5.7% of the proteins detected responded to the treatment. Many proteins that were differentially expressed were those found in, or associated with, the mitochondria. The proteomics data described in this article is associated with a research article, "Transcriptomic and proteomic analysis implicates the immune system and mitochondria as molecular targets of dieldrin in the zebrafish (Danio rerio) central nervous system" (A.M. Cowie, K.I. Sarty, A. Mercer, J. Koh, K.A. Kidd, C.J. Martyniuk, submitted) [1], and serves as a resource for researchers working in the field of pesticide exposures and protein biomarkers.

Dieldrin Augments mTOR Signaling and Regulates Genes Associated with Cardiovascular Disease in the Adult Zebrafish Heart (Danio rerio).

Dieldrin is a legacy organochlorine pesticide that is persistent in the environment, despite being discontinued from use in North America since the 1970s. Some epidemiologic studies suggest that exposure to dieldrin is associated with increased risks of neurodegenerative disease and breast cancer by inducing inflammatory responses in tissues as well as oxidative stress. However, the direct effects of organochlorine pesticides on the heart have not been adequately addressed to date given that these chemicals are detectable in human serum and are environmentally persistent; thus, individuals may show latent adverse effects in the cardiovascular system due to long-term, low-dose exposure over time. Our objective was to determine whether low-level exposure to dieldrin at an environmentally relevant dose results in aberrant molecular signaling in the vertebrate heart. Using transcriptomic profiling and immunoblotting, we determined the global gene and targeted protein expression response to dieldrin treatment and show that dieldrin affects gene networks in the heart that are associated with processes related to cardiovascular disease, specifically cardiac arrest and ventricular fibrillation. We report that genes regulating inflammatory responses, a significant risk factor for cardiovascular disease, are upregulated by dieldrin whereas transcripts related to lysosomal function are significantly downregulated. To verify these findings, proteins in these pathways were examined with immunoblotting, and our results demonstrate that dieldrin constitutively activates Akt/mTOR signaling and downregulates lysosomal genes, participating in autophagy. Our data demonstrate that dieldrin induces genes associated with cardiovascular dysfunction and compromised lysosomal physiology, thereby identifying a novel mechanism for pesticide-induced cardiotoxicity.

The legacy pesticide dieldrin acts as a teratogen and alters the expression of dopamine transporter and dopamine receptor 2a in zebrafish (Danio rerio) embryos.

Dieldrin (DLD) is a lipophilic pesticide that shows environmental persistence. The objectives were to determine the effects of DLD on GABAergic and dopaminergic systems in developing zebrafish. Both chorionated and dechorionated embryos (~24h post-hatch) were exposed to a single concentration of DLD (0.347-3470µM) for 48h. Following exposure, a subset of larvae was placed into clean water for 6days (i.e. depuration phase). Chorionated embryos showed <15% mortality while dechorionated embryos showed higher mortality (>30%), suggesting that the chorion protected the embryos. Over a 6day depuration phase, there was a dose dependent effect observed in both the "dechorionated and chorionated embryo" treatments for larval mortality (>60%). At the end of depuration, there was no detectable change in neuro-morphological endpoints that included the ratio of notochord length to body length (%) and the ratio of head area to body area (%). However, DLD did induce cardiac edema, skeletal deformities, and tremors. GABA-related transcripts were not affected in abundance by DLD. Conversely, the relative mRNA levels of dopamine transporter (dat1) and dopamine receptor drd2a mRNA were decreased in dechorionated, but not chorionated, embryos. These data suggest that DLD can alter the expression of transcripts related to dopaminergic signaling. Lastly, GABAA receptor subunits gabrB1 and gabrB2, as well as dopamine receptors drd1 and drd2a, were inherently higher in abundance in dechorionated embryos compared to chorionated embryos. This is an important consideration when incorporating transcriptomics into embryo testing as expression levels can change with removal of the chorion prior to exposure.

Molecular networks related to the immune system and mitochondria are targets for the pesticide dieldrin in the zebrafish (Danio rerio) central nervous system.

The objectives of this study were to determine the behavioral and molecular responses in the adult zebrafish (Danio rerio) central nervous system (CNS) following a dietary exposure to the pesticide dieldrin. Zebrafish were fed pellets spiked with 0.03, 0.15, or 1.8µg/g dieldrin for 21days. Behavioral analysis revealed no difference in exploratory behaviors or those related to anxiety. Transcriptional networks for T-cell aggregation and selection were decreased in expression suggesting an immunosuppressive effect of dieldrin, consistent with other studies investigating organochlorine pesticides. Processes related to oxidative phosphorylation were also differentially affected by dieldrin. Quantitative proteomics (iTRAQ) using a hybrid quadrupole-Orbitrap identified 226 proteins that were different following one or more doses. These proteins included ATP synthase subunits (mitochondrial) and hypoxia up-regulated protein 1 which were decreased and NADH dehydrogenases (mitochondrial) and signal recognition particle 9 which were up-regulated. Thus, proteins affected were functionally associated with the mitochondria and a protein network analysis implicated Parkinson's disease (PD) and Huntington's disease as diseases associated with altered proteins. Molecular networks related to mitochondrial dysfunction and T-cell regulation are hypothesized to underlie the association between dieldrin and PD. These data contribute to a comprehensive transcriptomic and proteomic biomarker framework for pesticide exposures and neurodegenerative diseases. BIOLOGICAL SIGNIFICANCE: Dieldrin is a persistent organochlorine pesticide that has been associated with human neurodegenerative disease such as Parkinson's disease. Dieldrin is ranked 18th on the 2015 U.S. Agency for Toxic Substances and Disease Registry and continues to be a pesticide of concern for human health. Transcriptomics and quantitative proteomics (ITRAQ) were employed to characterize the molecular networks in the central nervous system that are altered with dietary exposure to dieldrin. We found that transcriptional and protein networks related to the immune system, mitochondria, and Parkinson's disease were preferentially affected by dieldrin. The study provides new insight into the mechanisms of dieldrin neurotoxicity that may explain, in part, the association between this pesticide and increased risks to neurodegeneration. These data contribute in a significant way to developing a molecular framework for pesticide induced neurotoxicity.

Response of oxidative stress transcripts in the brain of wild yellow perch (Perca flavescens) exposed to an environmental gradient of methylmercury.

Methylmercury (MeHg) exposure and adverse health effects in fishes have been documented, but the molecular mechanisms involved in toxicity have not been fully characterized. The objectives of the current study were to (1) determine whether total Hg (THg) in the muscle was predictive of MeHg concentrations in the brain of wild female yellow perch (Perca flavescens) collected from four lakes in Kejimkujik National Park, a known biological mercury (Hg) hotspot in Nova Scotia, Canada and (2) to determine whether transcripts involved in the oxidative stress response were altered in abundance in fish collected across five lakes representing a MeHg gradient. In female yellow perch, MeHg in whole brain (0.38 to 2.00µg/g wet weight) was positively associated with THg in muscle (0.18 to 2.13µg/g wet weight) (R2=0.61, p<0.01), suggesting that muscle THg may be useful for predicting MeHg concentrations in the brain. Catalase (cat) mRNA levels were significantly lower in brains of perch collected from lakes with high Hg when compared to those individuals from lakes with relatively lower Hg (p=0.02). Other transcripts (cytochrome c oxidase, glutathione peroxidase, glutathione-s-transferase, heat shock protein 70, protein disulfide isomerase, and superoxide dismutase) did not show differential expression in the brain over the gradient. These findings suggest that MeHg may be inversely associated with catalase mRNA abundance in the central nervous system of wild fishes.

Molecular responses of Walleye (Sander vitreus) embryos to naphthenic acid fraction components extracted from fresh oil sands process-affected water.

Naphthenic acid fraction components (NAFCs) are constituents of oil sands process-affected water (OSPW), which is generated as a result of unconventional oil production via surface mining in the Athabasca oil sands region. NAFCs are often considered to be major drivers of OSPW toxicity to various taxa, including fishes. However, the molecular targets of these complex mixtures are not fully elucidated. Here we examined the effects in walleye (Sander vitreus) embryos after exposure to NAFCs extracted from fresh OSPW. Eleutheroembryos (exposed to 0, 4.2 or 8.3mg/L NAFCs from 1day post-fertilization to hatch) were subsampled, measured for growth and deformities, and molecular responses were assessed via real-time polymerase chain reaction (PCR). Fourteen genes were evaluated, with a focus on the aryl-hydrocarbon receptor (AhR) - cytochrome P450 pathway (arnt, cyp1a1), the oxidative stress axis (cat, gst, sod, gpx1b), apoptosis (e.g. casp3, bax and p53), growth factor signaling (e.g. insulin-like growth factors igf1, igf1b, and igf1bp), and tissue differentiation (vim). NAFC exposure was associated with an increase in the expression of cyp1a1, and a decrease in gpx1b and ribosomal protein rps40. These results indicate that NAFC effects on walleye early-life stages may be mediated through oxidative stress via pathways that include AhR.