Molecular toxicology approaches to address the adverse outcome pathway for narcosis toxicity using Caenorhabditis elegans and the RTgill cell line as model systems Erica K. Brockmeier 1 , Philipp Antczak 1 , Geoff Hodges 2 , Emma Butler 2 , Cecilie Rendal 2 , Steve Gutsell 2 , and Francesco Falciani 1 1 Institute of Integrative Biology, University of Liverpool, UK 2 Safety & Environmental Assurance Centre, Unilever, UK
WHAT DO WE KNOW ABOUT NARCOSIS? Kow-dependent and reversible Class 1 Class 2 Vaes et al. 1998 70,000 industrial chemicals • Loss of reaction to stimuli • Loss of equilibrium • Decreased respiration rate • Decreased metabolism • Lethality X
WHAT DO WE KNOW ABOUT NARCOSIS? Antczak et al., 2015 Correlation of significance with both Kow and thapsigargin exposures Calcium ATPase pump inhibitor Kow-dependent transcriptional switch ~log Kow 1.8 X Internal Lethal Conc. Narcotics: constant ILC50 for chemicals of log K ow > 2 (Escher 2002)
PROJECT OBJECTIVE AND SPECIFIC AIMS OBJECTIVE: Use C. elegans and the RTgill cell line as model systems for evaluating the underlying mechanisms of narcosis and determine if narcosis can be predicted using biological response data Specific aims 1. Compare species and system ( in vivo versus in vitro ) sensitivities between C. elegans and RTgill cells after exposure to a panel of narcotic chemicals 2. Determine if class 1 (nonpolar) and class 2 (polar) narcotic compounds differ in their molecular-level responses in C. elegans exposures 3. Identify potential membrane protein targets and impactesd pathways of narcotic compounds using C. elegans transcriptional data
TOOLS FOR EVALUATING THE MECHANISMS OF NARCOSIS in vivo model: C. elegans High- throughput EC50 screening with modified ‘touch test’ Advantages of C. elegans : – Gene knock-outs – RNA interference libraries – Transgenic strains – Motility and behavioral assays – Cell fate map – Neural networks Advantages of RTgill cell line: in vitro model: Rtgill cell line – High-throughput exposures and physiological assays (respiration, metabolism) – Single cell imaging – O. mykiss genome available – Connections to aquatic toxicity data and in vivo exposures High-throughput EC50 screening with CellTox green (Promega)
C. ELEGANS AND RTGILL EXPOSURES Exposures in liquid K- Exposures in L-15 media media (NaCl/KCl) (5% FBS, proliferative cells) 24h exposures 24h exposures Endpoint: Lethality Endpoint: Cytotoxicity Nominal RTgill Nominal C. elegans Adj R 2 = 0.585 Adj R 2 = 0.27 P-value = 3.23E-05 P-value = 0.0019 Panel of 30 narcotic chemicals, 15 from class 1 (non-polar) and 15 from class 2 (polar) Free RTgill Free C. elegans Significant regression with log Kow in both systems, and use of the TopLine model is Adj R 2 = 0.73 Adj R 2 = 0.821 P-value = 3.93E-07 P-value = 3.49E-12 able to reduce regression variation based on nominal concentrations
C. ELEGANS MICROARRAY EXPERIMENTS Worms transferred to exposure chamber 12h before exposure 1/10 LC50 exposure 1/10 LC50 exposure 1/10 LC50 exposure 1/10 LC50 exposure Panel of 30 narcotics: Day 1 chemicals and Day 2 chemicals and Day 3 chemicals and Day 4 chemicals and 15 class 1 and 15 class 2 controls (3) controls (3) controls (3) controls (3) Single microarray on pooled (N=3) biological replicates Full replication for control samples QC analysis, quantile normalization, Expression of Gene(i) Gene remove genes below background average Error model D1 Error model D2 Error model D3 Error model D4 Control variance distribution Individual chemical expression profiles Differential gene expression and statistical modelling pipeline
EXPLORATORY DATA ANALYSIS: ARE THERE GENE SETS THAT CAN DISTINGUISH POLAR AND NON-POLAR COMPOUNDS? What KEGG pathways contribute the most to the principal component loadings? KEGG pathway NES Score Carbon metabolism 3.01063 Pyruvate metabolism 2.96546 Arginine and proline metabolism 2.763 Glyoxylate and dicarboxylate metabolism 2.74236 Fatty acid degradation 2.72804 Citrate cycle (TCA cycle) 2.50691 Valine, leucine and isoleucine degradation 2.33069 Fatty acid metabolism 2.27663 Butanoate metabolism 2.19156 Propanoate metabolism 2.08354 Biosynthesis of amino acids 1.98238 Lysine degradation 1.92407 Biosynthesis of unsaturated fatty acids 1.86369 Peroxisome 1.81518 Tryptophan metabolism 1.72816 PCA of KEGG pathway scores, separated by Glycolysis / Gluconeogenesis 1.54885 class 1 (non-polar) and class 2 (polar) 2-Oxocarboxylic acid metabolism 1.4903 Alanine, aspartate and glutamate metabolism 1.29005 Cysteine and methionine metabolism 1.1557 Glycine, serine and threonine metabolism 1.09278 Energy and amino acid-related processes contribute Neuroactive ligand-receptor interaction -1.12733 Metabolism of xenobiotics by cytochrome P450 -1.45974 to the separation of class 1 and class 2 narcotics.
DIFFERENTIAL GENE EXPRESSION ANALYSIS: Significance Analysis of Microarrays: A small numbers of significant genes but are related to energy and metabolism Class 1 versus class 2: Low versus high log Kow: 134 significant genes (FDR 10%) 479 significant genes (FDR 10%) Time course SAM with Kow: Gene set t enri richment analy lysis is 68 significant genes (FDR 10%) bioinfo.cnio.es/ Are e there se sets of of genes whic ich ar are e mor ore or or les ess s pr present than expected by y ch chance? Transcriptional ‘switch’ ~ Log Kow 2.10 2.10
GSEA ANALYSIS: CLASS 1 VS CLASS 2 Significant KEGG pathways (1% FDR) comparing class 1 and class 2 narcotics (avg. enrichment score) GSEA enrichment score Overlaps with exploratory PCA KEGG analysis
PREDICTION ANALYSIS: CLASS 1 VS CLASS 2 Genetic Algorithm to Optimize problems related to variable selection: Able to distinguish class 1 from class 2 narcotics using individual genes Forward selection model 80% accuracy for NP and 92% for P Chemical incorrectly sorted Model uses eight individual genes: C04E12.2-Unknown membrane protein F14F7.3-CYP13A T10B9.2-CYP13A5 Chemical correctly sorted T16G1.6-unknown growth-related protein T16G1.7-Checkpoint protein T24B8.3-hypothetical protein B0464.8-embryo development Class 1 (non-polar) Class 2 (polar) F56A4.5-Checkpoint protein We can predict different classes of narcotics using gene expression patterns, and will next expand this model to include non-narcotics for screening procedures.
IDENTIFICATION OF POTENTIAL MEMBRANE PROTEIN TARGETS Individual chemical expression profiles Correlations Correlations between membrane gene between log Kow and gene expression and all other expression gene expression expression Narcosis = membrane perturbation But how? • Determine neighborhoods of membrane genes • Correlate at 0% FDR and correlation >= 0.7 • GSEA of genes which positively correlate with membrane genes • Enrichment scores for each membrane genes Antczak 2015: Role of CERCA pumps? What membrane genes are positively or negatively enriched in relation to Log Kow and narcosis-induced gene expression?
IDENTIFICATION OF POTENTIAL MEMBRANE TARGETS Positively enriched with Kow: Negatively enriched with Kow: 56 significant genes (0% FDR) 159 significant genes (0% FDR) What t fu funct ctions do o th these genes have? What t fu funct ctions do o th these genes have? While we see a large number of potential membrane targets, for validation we will focus on genes with functions relevant for narcosis-related endpoints.
NARCOTIC EXPOSURES INCREASE TIME TO PARALYSIS IN CO- EXPOSURES WITH LEVAMISOLE: IMPACTS ON ACHE SIGNALLING Class 1 narcotics ACh signalling Presynaptic 2HEE p=<0.001 neuron 1-Octanol p=0.012 1- hexanol p=0.004 ACh Postsynaptic neuron AChR Class 2 narcotics levamisole O-CRESOL p=<0.001 Receptor over-activation ANILINE p<0.01 C. elegans paralysis Octylamine <0.05 How are narcotic chemicals exerting a protective effect from the post-synaptic Work done by over-activation caused by levamisole? L. Boakes
HYPOTHESES ON POTENTIAL NARCOTIC TARGETS WHICH AFFECT ACHE SIGNALLING Presynaptic neuron In top 10 highest scoring negatively enriched genes Reduced AchR -> Less receptor over-activation -> protective effect during levamisole exposures ACh levamisole In top 10 highest scoring positively enriched genes Postsynaptic Ric-3 enrichment -> Changes to post-synaptic receptor AChR neuron turnover -> delayed paralysis GAR-3: muscarinic AchR DEG-3: subunit of • Required for nicotinic AchR RIC-3: Resistance to Inhibitors membrane potential of Cholinesterase via mobilizing calcium • Necessary for function of nicotinic acetylcholine receptors We are ready to validate these hypotheses using gene knock-out models to determine their role in narcosis toxicity.
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