Title: Gene expression analysis reveals chemicalspecific profiles
1Gene expression analysis reveals
chemical-specific profiles
Hisham K. Hamadeh, Pierre R. Bushel, Supriya
Jayadev, Karla Martin, Olimpia DiSorbo, Stella
Sieber, Lee Bannett, Raymond Tennant, Raymond
Stoll, J. Carl Barrett, Kerry Blanchard, Richard
S. Paules, and Cynthia A. Afshari
Jonathan Thomulka and Michelle Tsai
2What is this used for?
Gene expression profiling technology is used to
examine multiple genes and signaling pathways
Toxicogenomics field of science dealing with
information about gene and protein activity in
response to toxic substances
3Toxicity Techniques
One way is to study the change of gene expression
in response to chemical exposure using classical
methods of association Problem This cant be
used because chemical responses tend to be
frequently modulated by MANY compounds You would
have to look at many genes for these changes by
many compounds and at their collective state of
expression
Microarray
Concerted expression pattern across genes
constitutes the expression profile of a compound
at a certain dosage and time
4- Hypothesis
- Whether structurally unrelated compounds from the
same chemical class produced similar, yet
distinguishable, gene expression profiles - Compounds studied
- (all liver toxicants)
- 3 peroxisome proliferator class
- Clofibrate
- Wyeth 14,643
- Gemfibrozil
- 1 Enzyme inducer
- Phenobarbital
5- Methods
- Sample
- Rats each had a different dosage of a single
compound and how long they were administered
either 24 hours or 2 weeks - Histopathological analysis
- Physically looked at liver tissue
- RNA isolation
- RNA isolated using
- QIAGEN kits for real-time PCR
(normal rat liver tissue)
6- Methods cont.
- cDNA microarray hybridization and analysis
- cDNA Rat Chip, M13 primers
- (Control Cy3, sample Cy5)
- RNA labeled by reverse transcription
- reaction with Cy3 and Cy5 conjugates
- Fluorescently labeled cDNAs were mixed
- and hybridized simultaneously to the cDNA
microarray chip. - Chips scanned and analyzed by a computer
- Locates targets on the array
- Gets rid of background
- Identifies differentially expressed genes using a
probability-based method. - Calculated a 95 confidence interval for the
ratio intensity values - (Genes having a value outside this interval were
considered to be significantly differentially
expressed)
7- Methods cont.
- Real-time quantitative PCR
- RNA samples representing animals were used to
validate the expression profile of 10 genes
obtained from using cDNA microarray data - Performed reverse transcription and real-time PCR
of RNA at the same time, using custom primers
(reverse and forward) - Used a kit to detect double-stranded DNA
generated during PCR amplification which also
looked at fluorescent signals to monitor the
reaction
8- Results
- Just from looking at tissue
- 24 hour dosed animals No physical changes
- 2 week dosed animals these livers showed
hypertrophic hepatocytes (increased liver cell
size)
Animals treated with the 3 peroxisomes abundant
microvesiculated cytoplasm (lots of vesicles in
cytoplasm)
Animals treated with phenobarbital basophilic
stippling (dots)
9(No Transcript)
10(No Transcript)
11Table 2 cont.
12Validation of Experiment 1. Replicate analysis
3 animals were used for each compound and their
gene expression was measured on 3 chips. This
approach gave 9 measurements for each gene -gt
reduced the probability of getting false
positives 2. Routinely conducted re-sequencing
of the clones that were found to have significant
change. The clone set showed an accuracy of
90 3. Validated the expression profile of 10
genes across samples through high correlation
between cDNA microarray and RT-PCR (Table 3)
13Figure 1
RT-PCR products of the genes indicated to show
the quality of the reaction (validate)
14Compared cDNA microarray data with RT-PCR which
showed a high correlation -gt validated their
technique Induction or repression of each gene
was confirmed across multiple samples
15Figure 2
Application of hierarchical cluster analysis
confirmed that individual animals could be
distinguished by the class of toxicants to which
they were exposed
Revealed 2 distinct nodes containing animals
treated with either of the 2 classes of chemicals
Gene
Different Animals
Clustering based on expression pattern
Red gene induction Green gene repression
16Figure 3
Principal components analysis demonstrated close
proximity in the gene expression pattern between
the peroxisome chemicals, but distinct compared
to phenobarbital-exposed animals
17Figure 4 Correlation of gene expression profiles
of individual animals exposed to different
chemicals
18Figure 5
Animals
Genes
Red gene induction Green gene repression
19Genes whose transcripts were increased in
phenobarbital-exposed animals over controls (time
independent)
20Genes that are regulated in the same way for the
24 hours and 2 week test can be used in further
experiments as biomarkers when looking at gene
expression profiles
Below are different genes that were modulated by
peroxisome proliferators collectively in a
time-independent fashion
21Figure 6
Red Gene induction Green Gene
repression (relative to control)
22Transient and delayed alterations in gene
expression in response to the daily exposure to
the chemicals used in this study
A transiently altered transcripts (most
represented signaling related genes)
23B genes that required a delayed period of time
for regulation. Associated with adaptation events
24Figure 7
Yellow Past findings Blue New mechanistic
insights
25There is high concordance of the expression
changes found in the microarray analysis in
phenobarbital-exposed rats with the results
obtained by scientists using other methodologies
over many years of study
26Regulatory and molecular pathways affected by
phenobarbital
27Gene expression profile data has supported past
findings and revealed new relations
28- Potential Problems
- Whether animals should be grouped together as a
pool or examined individually in toxicogenomics
studies - Pooling and then using individual animals for the
verification steps can make microarray less
costly, but can cause misinterpretation of data
if one animal shows a distinct response or lack
of response - (This study analyzed individual chemically
exposed animals against a pool of control animals)
29- Conclusion
- Expression profiling can give a more
comprehensive overview of molecular responses to
toxicant exposure by revealing the coordinate
expression of multiple genes in homeostasis and
metabolic pathways - The agreement of the expression profiles for
these two chemical classes with past studies lend
confidence in the use of these gene expression
profiles in further pattern recognition
applications - There is an influence of the time of exposure on
gene expression - Data revealed gene expression that has not been
previously associated with the compounds they
used -gt could provide targets of mechanism
30Further Reading Eisen, M.B., Spellman, P.T.,
Brown, P.O., Botstein, D. (1998). Cluster
analysis and display of genome-wide expression
patterns. Proc. Natl. Acad. Sci. U.S.A.
9514863-14868. Fielden, M.R., Zacharewski, T.R.
(2001). Challenges and limitations of gene
expression profiling in mechanistic and
predictive toxicology. Toxicol. Sci.
606-10. Nuwaysir, E.F., Bittner, M., Trent, J.,
Barrett, J.C., Afshari, C.A. (1999). Microarrays
and toxicology The advent of toxicogenomis. Mol.
Carcinog. 24153-159. Torres, T.T., Muralidhar,
M., Ottenwalder, B., Schlotterer, C. 2008. Gene
expression profiling by massively parallel
sequencing. Genome Res. 18172-177.