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.

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.

Transcript variability and physiological correlates in the fathead minnow ovary: Implications for sample size, and experimental power.

Fundamental studies characterizing transcript variability in teleost tissues are needed if molecular endpoints are to be useful for regulatory ecotoxicology. The objectives of this study were to (1) measure transcript variability of steroidogenic enzymes and steroid receptors in the fathead minnow (FHM; Pimephales promelas) ovary to better determine normal variability and the sample sizes needed to detect specific effect sizes and to (2) determine how expression patterns related to higher level endpoints used in some regulatory ecotoxicology programs (e.g. relative gonad size). Estrogen receptor 2b (esr2b) and 5α-reductase a3 (srd5a3) showed high variability in the ovary (CV>1.0) while progesterone receptor (pgr), androgen receptor (ar), and esr2a showed comparatively low variability (CV=~0.5--0.7). Using these estimates, a power analysis revealed that sample sizes for real-time PCR experiments would need to be>20 to detect a 2-fold change for 7 of the transcripts examined; thus many molecular studies conducted in the fish ovary may have insufficient power to detect smaller effects. Two transcripts were correlated to steroid production in the ovary; cyp19a1 levels were positively correlated to in vitro E2 production, while ar levels were negatively correlated to in vitro T production. Thus, these transcripts may be informative molecular surrogates for ovarian steroid production. No transcript investigated showed any correlation to GSI, condition, or body weight/length. Molecular approaches in fish are increasingly used to assess biological impacts of chemical stressors; however additional studies are required that determine how molecular variability relates to higher level biological endpoints.

Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17ß-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species.