1. • Discovery Compound Differentiation using
Toxicogenomics
• Investigative miRNA Expression Analysis
Molecular Toxicology in
Drug Discovery at
AstraZeneca
Joe Milano
6/16/2011

2. Outline
 Introduction to our approach to microarray analysis
 Differentiating compounds based on renal transcript
profiles to support drug discovery project progression
 Establish miRNA analysis capability in AstraZeneca Safety
Assessment
 miRNA expression and target prediction to understand 2,5-
hexanedione testicular effects

3. Analysis Approach
Rat 230 2.0 Array
31000
Transcripts
ANOVA
p=0.05
Statistics
Final
Gene
List
Filter Low
Expressing Genes
Signal Detection
Algorithm
List Analysis
Ontology Enrichment
Pathways Analysis

4. Pathway Analysis
 Toxicity Analysis Workflow tool helps to analyze the
dataset(s) in view of toxicogenomics and drug response
information contained in the MetaCore database
 GeneGo toxic pathology biomarkers
 GeneGo toxicity processes
 GeneGo toxicity maps
 GO Processes
 GO Molecular functions
 GO Localizations

5. GeneGo Ontology Distribution Output
 X-axis
 -log(pValue) – the
statistical likelihood that a
subset of genes in an
ontology would appear in
a gene list.
 Y-axis
 Rank order by
significance for the
ontology.
p=0.05

6. Pathways and networks
 Pathway – well established
biochemical or signal
transduction map. Eg. Insulin
pathway, apoptosis pathway
 Network – an interactive map
that is drawn based on
interactions that have been
curated from the literature

7. Outline
 Introduction to our approach to microarray analysis
 Differentiating compounds based on renal transcript
profiles to support drug discovery project progression
 Establish miRNA analysis capability in AstraZeneca Safety
Assessment
 miRNA expression and target prediction to understand 2,5-
hexanedione testicular effects

8. Discovery Phase Compound
Differentiation
 Compound AZ123 has entered development with known
kidney tox.
 Presence of intracytoplasmic hyaline droplets
 Increase in urine volume, urine protein and urine NAG
 Indicative of renal tubular injury
 Follow-up investigative compounds A, B and C are being
evaluated using a 14-day rat tox study
 Similar structures and pharmacology
 One of these will be selected for further development

9. Solution (or at least part of the solution)
 Applied transcript profile analysis to differentiate
compounds and support a selection decision
 Results evaluated with standard pathology, clinical
chemistry and urine protein biomarkers of nephrotoxicity
(Kim1, NGAL, aGST etc.)
 Based on these analyses nephrotoxicity rank ordering
 Compound C > Compound A ≈ Compound B
 Selection of Compound A supported by toxicogenomics
analysis

10. Experimental Workflow
Male rats dosed daily for 14 days p.o. N=3
Total RNA isolated from whole kidneys
Transcript abundance assayed using
Affymetrix Rat 230 2.0 array
Pathway analysis on gene lists performed in GeneGo’s MetaCore
Data analyzed using GeneSpring GX

11. Affymetrix Data Analysis
 GeneSpring GX 10.0
 PLIER16 used for probe summarization
 Data was filtered based on low raw signal
 Statistical analyses (t-test or ANOVA) p=0.05
 Lethality at high dose for 2 compounds made statistical
analysis difficult
 Gene lists were analyzed in MetaCore’s Toxicity
Analysis Workflow using 1.3-fold threshold

12. Analysis Workflow
Compound A
Compound B
Compound C
Toxicity Analysis – Kidney
focus comparing all
compound transcript lists.
Compound differentiation
analyzing individual
compound lists
Gain understanding of the
relationships of individual
genes.

13. Toxicity Workflow- Compare All Lists
 Each show about the
same number of gene
changes
 Compound C shows
twice the number of
genes within 1.3-fold
threshold

14. Toxicity Analysis Workflow : All Gene Lists
p= 0.05
 Gene lists analyzed in MetaCore
using Toxicity Analysis – kidney
focus
 Most process not shared by all
kidney transcript profiles
 Suggestion that there is a effect on
cell division and xenobiotic
metabolism
 Need higher resolution to understand
similarities and differences
 Compounds differentiated by
analyzing individual compound
transcript profiles

15. Toxicity Workflow – Individual Analysis

16. Toxicity Analysis Workflow: Single
Compound Lists
 Compounds A and B ontology
distributions show enrichment for the
same top 4 endpoints
 Several genes induced in the top 3
ontologies for both Compounds A
and B show significant overlap with
CAR and PXR related pathways
 Compound C ontology distribution
shows
 Common endpoint-CAR mediated
regulation kidney
 Ontologies related to cell cycle
progression
 Transcriptional response similar
for compounds A and B different
for Compound C
Compound A
Compound B
Compound Cp=0.05

17. CAR Regulation of Xenobiotic Metabolism
 ABCC4 is induced by all 3
compounds
 Both transporters and
Phase II genes induced
by compounds A and B
 Compound C shows
induction of transporters
 Expresssion profiles
overlap but with variable
effect on CAR (PXR)
controlled genes.
1- A Low Dose
2-A High Dose
3-B Low Dose
4- B High Dose
5-C Low Dose
6-C High Dose

18. CAR Regulation of Xenobiotic Metabolism:
Individual Transcripts
 Most transcripts show induction less than 2-fold
 Not impressive changes when examined individually
 Biological relevance may be extracted from transcripts
that show low induction when put into biological context

19.  Compound C genes that
are enriched for processes
involved in cell cycle
progression
 This suggests a mitogenic
response in the kidney
 Pathology did not show
increased mitotic index
Cell Cycle Progression of Mitosis
Compound C Specific Network

20. Cell Cycle Regulation of Mitosis:
Individual Transcripts

21. GO Molecular Functions
 Both Compounds A and B show enrichments for
transcripts involved in glucuronosyltransferase
and glutathione functions
 Compound C shows enrichment for genes
involved in multidrug transporter activity and
xenobiotic transporter activity
 Driven by increase ABCC4, ABCC2 and MDR1
 High dose AUC is 3x higher than compounds A or B
 Suggests potential for drug accumulation at 14 days
Compound C
Compound A
p=0.05
Compound B

22. Pathology
 Kidney pathology findings note intracytoplasmic
hyaline droplets (arrow) for all compounds.
 Also seen with development compound AZ123
 Presumed to be a2 -globulin specific to the male rat
 Not used for human risk assessment
 No difference between compounds

23. Toxic Pathology Biomarkers
 Most ontologies for Compounds A and B
are not statistically significant.
 Compound C enrichments strongly imply
kidney tubular injury/necrosis
 Kidney hyaline droplet ontologies driven by
the expression of Slc11A2 and ALT.
Compound BCompound A
Compound C p=0.05

24. Clinical Chemistry
 Data from all compounds show increase in LDH,
albumin, aGST and GSTYb1 in urine
 Indicators of tubular damage
 Increased albumin has been associated with renal hyaline
droplets
 Also seen at same time point with AZ123
 Compound C data show Kim1 protein increase in
remaining high dose animal and robust induction of
Kim1 transcript at both doses

25. Data Summary
 Compounds A and B behave similarly with respect to toxicity networks
 Induction of Xenobiotic Response genes, UGTs, GST reductase
 Compound C is different from the other 2
 Induction of genes involved in cell cycle control suggesting a mitogenic
response – regenerative?
 While no difference was found by pathology, Compound C data show
induction of Kim1, enrichment for transcripts associated with renal tubular
damage and up-regulation of cell cycle control genes
 Nephrotoxicity rank ordering
 Compound C > Compound A  Compound B
 Compound B later found to be a mutagen (Ames) and clastogen (rat
micronucleus)
 These data support selection of Compound A to move forward

26. Outline
 Introduction to our approach to microarray analysis
 Differentiating compounds based on renal transcript
profiles to support drug discovery project progression
 Establish miRNA analysis capability in AstraZeneca Safety
Assessment
 miRNA expression and target prediction to understand 2,5-
hexanedione testicular effects

27. MicroRNAs (miRNAs)
 miRNAs
 Highly conserved, single stranded
RNAs (~22 nucleotides)
 Reduce protein expression by
reducing mRNA translation
 miRNA expression profiles can be
influenced by the cellular
environments
 Emerging serum-based
biomarkers in various biological
and toxicological processes
 Dicer – an endoribonuclease
cleaves pre-miRNA to mature
miRNA
 RISC – RNA induced silencing
complex
Nucleus
Cytoplasm
mRNAmRNA
Protein-coding gene miRNA gene
Pre-miRNA
Pri-miRNA
Dicer
Mature miRNA
RISC
RISC
AAAA
Translational inhibition / mRNA degradation
Ribosome
ORF

28. Using miRNAs for Mechanistic
Investigation and Biomarker Assessment
 miRNAs are known to have specific tissue expression
 Promising tissue specific biomarkers of toxicity
 Little is know about actual gene silencing targets and
regulated pathways for many miRNAs.
 Used 2,5-hexanedione, Sertoli cell-specific toxicant, to
examine whether miRNAs might be potential biomarkers of
testicular toxicity.
 Compared predicted target pathway ontologies to known
target pathway ontologies to confirm roles of miRNAs in
testis.

29. Experimental Design and Outcome
 14-day rat study using the testicular toxicant, 2,5-hexanedione in
drinking water ad libitum
 Left testis was taken for RNA isolation and miRNA analysis on ABI
Taqman miRNA array
 8 miRNAs were differentially regulated
 Applied two analysis strategies for miRNA evaluation
 miRNAs were entered into a publicly available target prediction
algorithm (miRDB) which generated a list of 375 predicted targets
 Analyzed in GeneGo’s MetaCore database for known targets and
interaction network construction yielding a list of 74 genes.
 Both analysis strategies suggest miRNA targets involved FSH
signaling, cell cycle and cell adhesion pathways.

30. Approaches for identifying miRNA targets,
and potential mechanistic biomarkers
Marshall Thomas et al. Nature Structural & Molecular Biology 17 ,1169 (2010)
In vitro
In vivo
In silico
Toxicants

31. Male rats dose 14 days with 2,5-HD via ad libitum drinking water n = 3
RNA isolated from whole testis were analyzed by ABI rodent miRNA TaqMan low density
array card A
Statistical analysis yielded 8 differentially expressed miRNAs
Predicted miRNA targets
determined using miRDB
Experimentally determined miRNA targets mined
from GeneGo’s MetaCore
Network generation
Ontology enrichment analysis using genes
from network generation
Ontology enrichment analysis using genes
from target prediction
Experimental Workflow

32. Dysregulated miRNAs
 All are down regulated
 miRNAs were entered into GeneGo’s MetaCore database
and used to build an interaction network
 Network consists of 75 genes
 Each miRNA was entered into miRDB for target prediction
 375 putative targets were pooled and entered into MetaCore for
Enrichment Analysis

33. Network Build with Differentially
Expressed miRNAs
 Differentially expressed miRNAs were used in network build
 Experimentally determined miRNA targets are highlighted by bold red
edges
 Two-step interactions with known targets are included in this network

34. Ontology Enrichment Analyses
Predicted Targets Empirical Targets
 Enrichment analyses show the FSH-β signaling network in common
and suggest involvement of cell cycle, cell adhesion and signal
transduction pathways.

35. Hypothylamic-Pituitary-Gonadal Axis
 FSH stimulates the maturation
of germ cells by stimulation of
Sertoli cells.
 Induces Sertoli cells to secrete
inhibin as part of a negative
feedback loop.

36. Toxic Pathology Comparison
 Testis related toxic
pathologies
 Predicted targets – 23 of 50
 Empirical targets – 25 of 50
 Strongly relates miRNAs to
testis function.

37. Common Predicted and Empirical
miRNA Targets
 FOG2 (Friend of GATA) – transcription factor
 Important regulator of hematopoiesis and cardiogenesis in mammals
 Also has a role in gonadal differentiation and sex determination
 Found in multiple cell lineages in both the ovary and testis
 TCF8 (Transcription factor 8) – transcriptional repressor
 Known to be an FSH-regulated gene in the ovary
 TGFβ2 – receptor ligand
 Plays an important role in multiple developmental processes
 Known to block Inhibin A binding
 Wee1 – protein kinase
 Negative regulator of entry into mitosis – G2/M transition
 Known to control the activity of M-phase promoting factor – CyclinB/Cdc2
by inhibitory phosphorylation
 Transcript is decreased in the testis of men with spermatogenic failure

38. Conclusions
 Predicted targets for 8 dysregulated miRNAs in the testis
show enrichment for biologically relevant pathways related
to 2,5-hexanedione toxicity
 Two strategies for assessing the biological context of 8
dysregulated miRNAs in the testis show enrichment for
genes involved in FSH-β signaling, a pathway critical to
Sertoli cells stimulation and Germ cell maturation.
 In silico approach demonstrates that miRNAs play
important roles in regulation of testicular function
 Combining miRNA profiling with interactome/pathway
analysis is a promising approach for identifying
biological/toxicologically-relevant miRNA-mRNA
interactions, and potential mechanistic miRNAs biomarkers
 Further study of miRNAs in plasma and testis is ongoing

39. Acknowledgements
 Hank Lin
 Brandon Jeffy
 Yvonne Dragan
 Linda Barone

40. The PLIER Algorithm
 PLIER produces an improved signal (a summary value for a
probe set) by accounting for experimentally observed patterns
for feature behavior.
 Quantile normalization
 Raw intensity values are preprocessed to create equally distributed
data between chips.
 Estimation of background
 The intensity of the MM probe is treated as background and is
subtracted from PM probe.

41. GeneGo Process Networks
 Reproduction FSH-beta signaling pathway
 Effect of FSH on sertoli cell function
 BMP_TGF beta signaling
 Overlap with FSH signaling pathway – activin and BMP
 Cell cycle_G2-M
 Cell adhesion – cadherin, catenin and ephrin
p=0.05