TOXICOLOGICAL CATEGORIZATION OF P- AND E-SERIES GLYCOL … · introduction. materials and methods....
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INTRODUCTION
MATERIALS AND METHODS
CONCLUSIONS
RESULTS
FUTURE DIRECTIONS
TOXICOLOGICAL CATEGORIZATION OF P- AND E-SERIES GLYCOL ETHERS USING HIGH-CONTENT SCREENING OF HUMAN INDUCED PLURIPOTENT STEM CELL (iPSC)-DERIVED CELLS
Iwata Y1, Grimm FA1, Wilson M1, Chappell GA1, Bittner M2, Sirenko O3, Rowlands JC4, Ball N4, Rusyn I11Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA; 2Engineering Experiment Station, Texas A&M University, College Station, TX, USA;
3Molecular Devices, LLC, Sunnyvale, CA, USA; 4The Dow Chemical Company, Midland, MI, USA
• High-content screening (HCS) assays utilizing novel organotypiccell culture models1,2,3,4 are an attractive approach for chemicalsafety assessments using biological data-based category read-across.
• In order to test the hypothesis that HCS represents a feasibleapproach to categorize chemicals based on similarities in theirin vitro toxicity profiles, we applied a series of phenotypic andtranscriptomic screening assays to screen eight propylene (P-series) and twelve ethylene (E-series) glycol ethers, a structurallyrelated yet toxicologically diverse group of prototypical industrialhigh production volume chemicals.
• We used two human induced pluripotent stem cell (iPSC)-derived cell types, cardiomyocytes and hepatocytes. Cells wereexposed to glycol ethers for various durations in concentration-response.
• Concentration-response data from phenotypic screening assayswere used to derive point-of-departure values5 that were usedas quantitative descriptors for categorization in ToxicologicalPrioritization Index (ToxPi).
P-Series and E-Series Glycol Ethers
The P-series glycol ether category
Mono- Di- Tri-
Methyl Ether PGME DPGME TPGME
Ethyl Ether
Propyl Ether PGnPE DPGnPE
Butyl Ether PGBE DPGBE TPGBE
•Data-rich, well established category (e.g., OECD acceptance)
•Read across used to address several endpoints within category
•Low order of toxicity throughout
•Toxicity limited to liver/kidney hypertrophy (adaptive adverse)
Mono- Di- Tri-
Methyl Ether EGME DEGME TEGME
Ethyl Ether EGEE DEGEE TEGEE
Propyl Ether EGPE
Butyl Ether EGBE DEGBE TEGBE
Hexyl Ether EGHE DEGHE
•Data-rich, well established category (e.g., OECD acceptance)
•Read across already used for several endpoints within the category
•Significant variance in toxicity exists (includes DART, immunotoxicity, blood toxicity, hepatotoxicity)
• In general, acid metabolite (e.g., methoxyacetic acid) is the toxicant
High-Content in vitro Screening for P-Series and E-Series Glycol Ethers using iPSC Cardiomyocytes and Hepatocytes
384-well plate design
2x or 5x working dilutions
Experimental Design Data Acquisition
Chemical Master Plate
20 Glycol Ethers2 Symple Glycols
Data Evaluation
EXPERIMENT 1
ImageXPress® XLS Micro
High
-Con
tent
Cel
l Im
agin
g
Cytotoxicity
EXPERIMENT 1
Nucl
ei
Hoe
chst
Mito
chon
dria
Mito
Trac
ker
ROS
form
atio
n
CellR
OX
Nor
mal
izatio
nDo
se-R
espo
nse
Toxicity Profiling
Cardiophysiology
Calc
ium
-Flu
x
FLIPR Tetra®
Cytotoxicity
Nuc
lei
Hoe
chst
Mito
chon
dria
Mito
Trac
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EXPERIMENT 2 EXPERIMENT 2Hepatocyte physiology
Cyto
skel
eton
Phal
loid
in
Lipi
d Ac
cum
ul.
Lipi
dTO
X
iCell HepatocytesiCell Cardiomyocytes
0 10 mM0 10.10.010.0010.0001
Nuc
lei
100 mM301010.10.01
High-Throughput Gene Expression Analysis Data Integration/ Visualization
•A targeted RNA sequencing technology, TempO-Seq™ (BioSpyder Technologies, Inc., Carlsbad, CA), were used.
• iPSC hepatocytes were exposed to test chemicals in concentration-response for 48 hours.
•mRNA targets were “barcoded” by a specific primer pair in PCR process for the following sequencing step.
•Libraries are sequenced using a HiSeq 2500 Ultra-High-Throughput Sequencing System (Illumina, San Diego, CA).
mRNA
Detector oligo annealing
Detector oligo ligation
PCR with barcodedprimers
Sequence
Concentration-response plots for representative phenotypes cardiomyocyte peak frequency (A) and viability (B) are shown. Plots are shown in each alcohol group. Data were normalized to media controls (100%). Data points represent means of duplicate
determinations (n=2). [BPM = beats per minute, cardiomyocyte beat frequency, Total cells (Hoechst) = the total number of nuclei]
In vitro toxicity profile categorization was performed using the ToxPi approach. ToxPi data derived from all endpoints and cell-types were integrated to a ToxPi score. The higher the ToxPi score, the higher the relative toxicity of the glycol ethers. There is a positive correlation between ToxPi scores and the length of the alcohol group (simple glycols, methyl-, ethyl-, propyl-, butyl-, and hexyl ethers). This trend was more prominent
than when the data was expressed by individual cell-type.
• There was a correlation between the length of the alcohol group and induced effects such that glycol ethers can be categorized based on the alcohol group.
• Within each alcohol group, there was a trend of increasing cytotoxicity between mono-, di-, and tri-substituted glycol ethers.
• These trend were more prominent when cell typed and all endpoints were integrated.• However, these trends did not align well with trends based on in vivo toxicity data with P- and E-series glycol ethers.• Gene expression analysis using TempO-Seq revealed clustering trends of E-series glycol ethers and simple glycols.• There was not a clear separation between E- and P- series glycol ethers in terms of biological activity using in vitro data.
• To conduct a biological and physico-chemical data-integrative categorization to further improve product groupings• To assess the relevance of the in vitro concentrations to in vivo dose• Pathway-integrative differential gene expression analysis to provide insights for mechanistic toxicology evaluation
ACKNOWLEDGEMENTSThis work was supported by institutional funding from Texas A&M University. Fabian Grimm is a recipient of SOT Colgate-Palmolive postdoctoralfellowship. We would like to thank the Dow Chemical Company for providing glycol ethers and reagents used in this study. In addition, we wouldlike to acknowledge technical assistance from Dr. Michael Bittner (Texas A&M University). We Thank BioSpyder for providing assistance withtranscriptomics data analysis.
REFERENCES
Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers
Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers
Hexyl Ethers; Eor
Butyl Ethers; P
Hexyl Ethers; Eor
Butyl Ethers; P
Concentration-response plots for representative phenotypes hepatocyte cytoskeletal integrity (A) and viability (B) are shown. Plots are shown in each alcohol group. Data points represent means of duplicate determinations (n=2). [% positive (phalloidin) = percentage of
cells with intact cytoskeleton, Total cells (Hoechst) = the total number of nuclei]
A
B
Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers
Simple Glycols & Methyl Ethers Ethyl & Propyl Ethers Butyl & Hexyl Ethers
Hexyl Ethers; E& Butyl Ethers; P
Butyl Ethers; E
Hexyl Ethers; E
Methyl Ethers; P Propyl Ethers; P
Ethyl & Propyl Ethers; E
A
B
Concentration Response Profiling - iPSC Cardiomyocytes
Concentration Response Profiling - iPSC Hepatocytes
Biological data-based read-across of P-Series and E-Series Glycol Ethers using in vitro data from iPSC cells
•Data were normalized to media controls and fitted to a concentration-response.
•To determine point-of-departure (POD) values at 1 standard deviation from baseline level, we used a custom script in R.
•POD values were transformed and visualized in ToxPi6.
•The larger the area of a ToxPi slice (on a 0-1 scale), the higher the toxicity of a substance on the respective phenotype.
1. Sirenko et al. (2013) J Biomol Screen. 18: 39-53 2. Sirenko et al. (2013) Toxicol Appl Pharmacol. 273: 500-507 3. Sirenko et al. (2015) Assay Drug Dev Technol. 12: 43-52 4. Grimm et al. (2015) Assay Drug Dev Technol. 13: 529-5465. US EPA (2011) Benchmark Dose Technical Guidance Document6. Reif et al. (2013) Bioinformatics. 29:402-403
Cell culture: iCell Cardiomyocytes and iCell Hepatocytes 2.0 were plated and maintained in 384-well plates.
Cardiophysiology screening: Cardiac physiological change were assessed by measuring the intracellular Ca2+-flux of synchronously beating iPSC cardiomyocytes using the FLIPR Tetra (Molecular Devices).
Treatments: Cells were incubated at the following concentrations for up to 48 hours. Cardiomyocyte screening: 0.01, 0.1, 1, 10, 30, 100 mM; Hepatocyte screening: 0.0001, 0.001, 0.01, 0.1, 1, 10 mM.
High-content imaging Screening: We conducted cytotoxicity by using the ImageXpress Micro XL high-content imaging system (Molecular Devices). Nuclei and mitochondria integrity were assessed for both cell types. Cytoskeletal integrity, reactive oxygen species formation (ROS) and lipid accumulation were assessed for hepatocytes.
Principal components analysis (PCA) for global changes in hepatocyte gene expression at the highest concentration is shown. There was a clustering trend of E-series glycol ethers and simple glycols. [A: 2D plot of PCA 1 and 2 ; B: 2D plot of PCA 1 and 3]
Gene Expression Profiling by TempO-Seq - iPSC Hepatocytes
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A B
Simple Glycols SimpleGlycols
E-Series
E-Series
The E-series glycol ether category
Hex
yl-
EGHEDEGHE
E
PGBEDEGBEEGBETEGBE DPGBETPGBE
E P
But
yl-
EGPE PGnPEDPGnPE
E P
Simple Glycols
Ethy
l-
EGEEDEGEETEGEE
E
Met
hyl-
PGMEDEGMETEGMEEGME DPGMETPGME
E P
EG PG
E P
Prop
yl-
Lege
nd
Methyl Ethers; P Propyl Ethers; P
Butyl Ethers; E