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ADVERSE OUTCOME PATHWAYS UNDERLYING NARCOSIS TOXICITY AND THE
USE OF –OMICS TOOLS FOR SCREENING POTENTIAL NARCOTIC CHEMICALS
Erica K. Brockmeier1, Danilo Basili1, Geoff Hodges2, Emma Butler2, Steve Gutsell2, Philipp Antczak1, and Francesco Falciani1
1Institute of Integrative Biology, University of Liverpool, UK2Safety & Environmental Assurance Centre, Unilever, UK
WHAT DO WE KNOW ABOUT NARCOSIS?
Kow-dependent and reversible
X
• Loss of reaction to stimuli• Loss of equilibrium• Decreased respiration rate• Decreased metabolism• Mortality
70,000 industrial chemicals
Vaes et al. 1998
Verhaar et al. 1992Classification of narcotics
WHAT DO WE KNOW ABOUT NARCOSIS?Antczak et al., 2015
Kow-dependent transcriptional switch
Calcium ATPase pump inhibitor
Correlation of significance with both Kow and thapsigarginexposures
Polar
Non-polar/baseline
amine ester ???
THE NARCOSIS ADVERSE OUTCOME PATHWAYAnkley et al., 2010
Macro-molecular interactions
• What are the molecular-level interactions?
• What are the potential protein targets involved?
Molecular Initiating Event
Accumulation in biological membranes
Disruption of membrane
integrity
Narcosis leading to respiratory
failure
Key Event
Better estimate of hazard and more confident risk
assessments of chemicals
Is it a narcotic?
YES
NO
Narcotic-specific risk assessment
NO Risk assessment based on MOA
PROJECT OBJECTIVE AND SPECIFIC AIMS
OBJECTIVE: Use systems biology and –omics datasets to provide insights into the mechanism of narcosis and to develop a tool for screening novel and potentially narcotic chemicals
Specific aim: Identify characteristic gene expression signatures using the model organism Caenorhabditis elegans after in vivo exposures to a panel of narcotic compounds, including polar, nonpolar, and other classes of narcotics categorized by current classification tools
Which genes and pathways underlie the biological responses to ‘narcotic’ exposure?
Can we distinguish different classes of ‘narcotic’ compounds using gene expression?
Question 1:
Question 2:
C. ELEGANS AS A MODEL SYSTEM
Advantages of C. elegans:
– Gene knock-outs
– RNA interference libraries
– Transgenic strains
– Motility and behavioral assays
– Cell fate map
– Neural networks
1) Sequential scans to track movementHigh-throughput EC50 screeningmethod developed: 2) Validated against standard method
3) EC50 results: Free concentration doses correlate to Kow
EXPERIMENTAL DESIGN
RNA extraction, labelling, hybridizationN=1 biological replicate*
QC analysis, normalization,remove lowly expressed genes
45 narcotics1/10th EC5024 hours
Identification of differentially expressed genes
Significance Analysis of Microarrays (SAM), Predictive Analysis of
Microarrays (PAMr), rank product, significantly enriched pathways
1. Which genes and pathways underlie the biological responses to
narcotic exposure?
Prediction analysis
Multivariate statistical models and molecular
QSAR analysis
2. Can we distinguish different classes of narcotic compounds
using gene expression? – i.e. do these differences exist
biologically?
Corrected gene expression
against vehicle controls with error model*
Control variance distribution
Gene average
Exp
ress
ion
of
Gen
e(i)
Are there genes that are unique to each category?• Prediction analysis for microarrays (PAMr) between polar and non-polar narcotics
No
n-P
ola
r
Po
lar
1. WHICH GENES AND PATHWAYS UNDERLIE THE BIOLOGICAL RESPONSES TO NARCOTIC EXPOSURE?
Gene set enrichment analysis (GSEA) results
Pathways overlapping with
GSEA in rank product analysis
(both 5% FDR)
448 genes significantly changed over gradient of Kow values (FDR 1%)
KEGG pathway# of
genesFold
EnrichmentBH P-value FDR
cel00980:Metabolism of xenobiotics by cytochrome P450 9 17.7 2.44E-07 6.32E-06
cel00480:Glutathione metabolism 9 13.15 1.65E-06 8.52E-05cel00982:Drug metabolism 8 14.11 5.33E-06 4.14E-04
Increasing Kow
Individual genes
NP PNP NPNP NPNPP P P PP PNP NP NPP P P P P P NP NP NPNP P NP P P NP
1. WHICH GENES AND PATHWAYS UNDERLIE THE BIOLOGICAL RESPONSES TO NARCOTIC EXPOSURE?
What role does hydrophobicity play in differential gene expression?• Quantitative significance analysis of microarrays over Log Kow
1. WHICH GENES AND PATHWAYS UNDERLIE THE BIOLOGICAL RESPONSES TO NARCOTIC EXPOSURE?
Selecting genes for follow-up analysis: WORMPATH(Example with KOW SAM)
Network score: 2.12p-value (list): 0.007205
Expressed in sensory neurons, regulates responses to environment to maintain energy balance, analogous to bmp ligand in humans
amino acid/nucleotide biosynthesis
Ortholog of FOXO, TF for aging regulation
human orthologis Ca-dependent nucleotidase IGF-1 receptor,
stress resistance
heat shock co-chaperones, stress response
Input: Differential gene expression listsOutput: Networks with score based on average number of citations supporting each edge and list score (over-representation of list)
2. CAN WE DISTINGUISH DIFFERENT CLASSES OF NARCOTIC COMPOUNDS USING GENE EXPRESSION?
80% CORRECT CLASSIFICATION WITH GENES ALONE!
Incorrectly classified chemicals: Insights on classic classification schema, can use
data towards improving current methods
trichlorophenol otolunitrile
2-hydroxyethyl ether
Dodecyl tetraethylene glycol ether
trichlorobenzenebutanol
GALGO: Genetic Algorithm to Optimize problems related to variable selection• Supervised multivariate approach
Category Term Genes B-H pvalue
GOTERM_MF_FAT Structural constituent of cuticle
7 1.0E-03
PIR_SUPERFAMILY Cuticle collagen 4 1.5E-03
DAVID pathway enrichment
Class confusion: How well the best-performing model classified each chemical
1. KEGG pathway mapping of expression data• Use first three principal components of
pathways with 5 or more genes2. Dragon chemical features
• 2815 descriptors in total
Questions to be addressed?• Which pathways are linked to what PFCs?• Can we predict LC50 based on molecular QSARs?
2. CAN WE DISTINGUISH DIFFERENT CLASSES OF NARCOTIC COMPOUNDS USING GENE EXPRESSION?
Molecular QSAR approach (Antczak et al., 2015)
(Antczaket al. 2015)
Can we use additional chemical features to help us improve our classification of narcotics based on gene expression?
Alanine, aspartate and glutamate
metabolism
Arginine and prolinemetabolism
Lysine degradationPhenylalanine
metabolism
Tyrosine metabolism
Valine, leucine and isoleucine
degradation
Nicotinate and nicotinamide metabolism
One carbon pool by folate
Purine metabolism
Retinol metabolismRiboflavin
metabolism
Selenocompoundmetabolism
Ubiquinone and other terpenoid-
quinonebiosynthesis
Pyrimidine metabolism
Proteasome
Protein processing in endoplasmic reticulum
RNA degradation
SNARE interactions in vesicular transport
Ubiquitin mediated proteolysis
Aminoacyl-tRNAbiosynthesis
RNA transport
Ribosome
Ribosome biogenesis in eukaryotes
Nucleotide excision repair
RNA polymerase
mRNA surveillance pathway
Basal transcription factors
Base excision repair
DNA replication
Mismatch repair
Amino sugar and nucleotide sugar
metabolism
Ascorbate and aldarate metabolism
Butanoatemetabolism
Citrate cycle (TCA cycle)
Fructose and mannose metabolism
Glyoxylate and dicarboxylatemetabolism
Metabolic pathways
Nitrogen metabolism
Sulfur metabolism
Inositol phosphate metabolism
Oxidative phosphorylation
ABC transporters
ErbB signalingpathway
FoxO signalingpathway
Phosphatidylinositol signaling system
TGF-beta signalingpathway
Wnt signaling pathway
mTOR signalingpathway
Dorso-ventral axis formation
Notch signalingpathway
Hedgehog signalingpathway
MAPK signalingpathway
Arachidonic acid metabolism
Fatty acid biosynthesis
Fatty acid degradation
Fatty acid metabolism
Glycosaminoglycan biosynthesis - heparan
sulfate / heparin
N-Glycan biosynthesis
Sphingolipid metabolism
Fatty acid elongation
Glycerophospholipidmetabolism
Glycosylphosphatidylinositol(GPI)-anchor
biosynthesisDrug metabolism -cytochrome P450
Drug metabolism -other enzymes
Lysosome
Metabolism of xenobiotics by
cytochrome P450
Regulation of autophagy
Amino Acid Metabolism
Other Metabolism
Folding, Sorting, Degradation
Translation, Transcription,
Repair
Carbohydrate Metabolism and
Energy
Xenobiotic Degradation
and Transport
Signalling and Development
Lipid and Glycan Metabolism
Interactions of functional clusters in C. elegans dataset
2. CAN WE DISTINGUISH DIFFERENT CLASSES OF NARCOTIC COMPOUNDS USING GENE EXPRESSION?Linked to PCF
Linked to EC50
Linked to PCF and EC50
In daphnia dataset
Daf-7 pathway
CONCLUSIONS AND FUTURE DIRECTIONSWHAT WE KNOW SO FAR
– WHICH GENES AND PATHWAYS UNDERLIE THE BIOLOGICAL RESPONSES TO NARCOTIC EXPOSURE?
• Biological enrichment between polar and nonpolar narcotics and over log Kow ranges
• WormPath and other online databases to guide follow-up studies
– CAN WE DISTINGUISH DIFFERENT CLASSES OF NARCOTIC COMPOUNDS USING GENE EXPRESSION?
• 80% classification of polar versus nonpolar using gene signatures alone
• Pathway-level responses can be predicted using PFCs and related to D. magna response
BETTER PREDICTIONS
In vitrotoxicity
In vivotoxicity
Population relevant
MIE KE AO
BETTER CLASSIFICATION
Polar
Non-polar Amine Ester
Other
NEXT STEPS
– C. elegans targeted gene knock-outs to validate role of key genes in toxicity
– Cross-species comparisons between D. magna, C. elegans and RT gill cell line microarrays
– Look at other MoA chemicals (‘unspecific reactives’)
ACKNOWLEDGMENTS
• Francesco Falciani and our lab group
– Special thanks: Philipp Antczak and Danilo Basili
• Unilever and the SEAC
– Special thanks: Geoff Hodges, Emma Butler, Steve Gutsell, Chris Sparham, Cecilie Rendal
• Mark Cronin (LJMU)
• Mark Viant (University of Birmingham)
• University of Liverpool Physiology department/’Red Block’
THANK YOU! ANY QUESTIONS?
Control 1Control 2Control 3
Ethanol 1Ethanol 2Ethanol 3
Replicated samples used to validate N=1 screening
Control variance distribution
Ethanol average
Exp
ress
ion
of
Ge
ne
(i) 1. Error model
2. Linear models for microarray data
List of genes withsignificant p-values(p < 0.05, FDR 5%)
Control 1Control 2Control 3
Ethanol 1Ethanol 2Ethanol 3
*Main difference from one-way ANOVA: variance estimate shrunk towards mean (in ANOVA it uses sample variance), good for low replication microarray studies
What is similar between thesetwo methods, and is the errormodel applicable for findingdifferentially expressed geneswith an N=1?
ERROR MODEL VALIDATION
Similar and significant functional terms within DAVID pathway analysis
# of genes Correctedp-value
Glycolysis / Gluconeogenesis 12 (error model)12 (Limma)
5.43E-45.68E-4
Glyoxylate and dicarboxylate metabolism 66
7.21E-47.17E-4
Pyruvate metabolism 98
0.00120.0074
Arginine and proline metabolism 108
0.00120.0239
Propanoate metabolism 97
0.00470.0785
Citrate cycle (TCA cycle) 910
0.00700.0028
Fructose and mannose metabolism 86
0.00660.1247
Biosynthesis of unsaturated fatty acids 47
0.42590.0064
11651268
Ethanol.avgError model
Limmaanalysis
670495 598
1076 682
Limmaanalysis
318
UpregulatedDownregulated
364758
Ethanol.avgError model
While the exact genes are different, biological processesare similar between the replicated and N=1 approach, so we can use the error model as a screening tool for differential gene expression.
ERROR MODEL VALIDATION
Rank product analysis between polar and non-polar narcotics• Non-parametric test based on the ranking of fold changes between groups
1. WHICH GENES AND PATHWAYS UNDERLIE THE BIOLOGICAL RESPONSES TO NARCOTIC EXPOSURE?
Category Term Number of genes BH p-value
GOTERM_MF_FAT Structural constituent of cuticle 29 2.6E-27
GOTERM_MF_FAT Structural molecule activity 30 4.0E-15
GOTERM_BP_FAT Oxidation and reduction 14 3.6E-04
SMART Choline kinase family 5 2.3E-04
Up in polar: 214 genes (5% FDR), DAVID pathway analysis:
Category Term Number of genes BH p-value
GOTERM_MF_FAT Electron carrier activitiy 13 1.3E-04
INTERPRO Cytochrome P450 10 3.6E-06
GOTERM_BP_FAT Cellular response to unfolded protein 6 3.4E-06
GOTERM_BP_FAT Lipid modification 7 4.4E-04
Up in nonpolar: 209 genes (5% FDR), DAVID pathway analysis:
2. CAN WE DISTINGUISH DIFFERENT CLASSES OF NARCOTIC COMPOUNDS USING GENE EXPRESSION?
Linked to PCF
Linked to toxicity
Linked to PCF and toxicity
Interactions of functional clusters in D. magna narcosis dataset (Antczak 2015)
