• Discovery Compound Differentiation using Toxicogenomics

• Investigative miRNA Expression Analysis

Molecular Toxicology in Drug Discovery at AstraZeneca

Joe Milano6/16/2011

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

Analysis ApproachRat 230 2.0 Array

31000Transcripts

ANOVAp=0.05

Statistics

FinalGeneList

Filter LowExpressing Genes

Signal DetectionAlgorithm

List Analysis

Ontology Enrichment

Pathways Analysis

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

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

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

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

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

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

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

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

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.

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

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

Toxicity Workflow – Individual Analysis

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

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 Dose2-A High Dose3-B Low Dose4- B High Dose5-C Low Dose6-C High Dose

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

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

Cell Cycle Regulation of Mitosis:Individual Transcripts

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

Pathology

Kidney pathology findings note intracytoplasmic hyaline droplets (arrow) for all compounds.

Also seen with development compound AZ123 Presumed to be 2 -globulin specific to the male rat Not used for human risk assessment

No difference between compounds

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

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

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

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

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

DicerMature miRNA

RISC

RISCAAAA

Translational inhibition / mRNA degradation

Ribosome

ORF

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.

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.

Approaches for identifying miRNA targets, and potential mechanistic biomarkers

Marshall Thomas et al. Nature Structural & Molecular Biology 17 ,1169 (2010)

In vitroIn vivo

In silico

Toxicants

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

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

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

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.

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.

Toxic Pathology Comparison Testis related toxic

pathologies Predicted targets – 23 of 50 Empirical targets – 25 of 50

Strongly relates miRNAs to testis function.

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

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

Acknowledgements

Hank Lin Brandon Jeffy Yvonne Dragan Linda Barone

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.

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