Dr Nicholas Shackel - Bioinformatics and Personalised Medicine
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Bioinformatics and Personalized Medicine
Nicholas A. Shackel
1 A.W. Morrow Gastroenterology and Liver Centre Royal Prince Alfred Hospital
2 Liver Laboratory, Centenary InstituteSydney, NSW, Australia
3 Medicine University of SydneySydney, NSW, Australia.
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Overview
• Genome / Transcriptome
• Understanding disease
– mRNA Expression
– miRNA Expression
• Personalised medicine
• New technologies
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Bioinformatics
• A long term goal of Bioinformatics is to discover the causal processes among genes, proteins, and other molecules in cells
• This can be achieved by using data from High Throughput experiments, such as microarrays, deep-sequencing and proteomics
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Functional Genomics
Cell Nucleus
Chromosome
Protein
Graphics courtesy of the National Human Genome Research Institute
Gene (DNA)Gene (mRNA), single strand
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Systems Biology
New Paradigm
“ The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration..."
(Sauer Science April 2007)
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Genome
• 3 billion bases (x2)
• 1.5% protein encoding = 23,000 unique proteins
• >100,000 alternate splicing and post translation protein variants
• 1.5-8% of the genome has regulatory elements– UTRs, Promoters etc
• Single Nucleotide Polymorphism (SNP) 1:100 – 1:1000
• 90% “Junk” DNA– Unrecognized regulatory elements?– Entropy rate for coding and non-coding regions different
• Transcription without translation
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Transcriptomes
• Total transcriptome (mRNA pool)– SAGE ~ 100 000 (www.sagenet.org)
– UniGene 86 820 (Build 193)
• Organ transcriptomes (Velculescu et. al. 1999 Nature Genetics 23 p387)
– Brain - 46 %
– Liver - 26 %• “Liverpool” Liver array (Coulouarn et al. 2004 Hepatology 39 p353)
– 12638 transcripts
• Normal colon – 32% -> Diseased colon - 50%
• Understanding the liver transcriptome (Anderson et. al. 1997 Electrophoresis 18 p533)
– Secreted and abundant transcripts over represented in mRNA (29/50 mRNA vs. 0/50 protein)
• Cell transcriptomes 5000 to > 15000 genes (lymphocyte ~ 12 000 genes)
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Gene Regulation and Expression
Post Translational Mechanisms
Alternate Splicing / ncRNA
Epigenetic regulation
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Understanding Disease
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HCC Pathogenesis
Saffroy (2007) Clin Chem Lab Med 45(9): 1169
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HCV Genotype 1 vs Genotype 3 Clustering
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Gene Expression and Outcome
in HCC
Hoshida (2008)NEJM: 1
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Chromosomal Aberrations
Pie (2009) Acta Biochim Biophys Sin: 1
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mRNA profiling of HCV-induced cirrhosis and HCC - Hierarchal Clustering
HCC Cirrhotic (F4)
Donor
Cir
rhoti
c G
1
HC
C G
1H
CC
G3
HC
C G
1C
irrh
oti
c G
3C
irrh
oti
c G
3H
CC
G_
HC
C G
_+H
BV
HC
C G
3H
CC
ALD
HC
C G
3H
CC
ALD
HC
C A
LDH
CC
G3
Cir
rhoti
c G
1C
irrh
oti
c G
1H
CC
G1
Cir
rhoti
c G
3C
irrh
oti
c G
3H
CC
G4
+H
BV
Cir
rhoti
c G
1H
CC
G1
Cir
rhoti
c G
3C
irrh
oti
c G
1C
irrh
oti
c G
3H
CC
G1
HC
C G
3C
irrh
oti
c A
LDC
irrh
oti
c A
LDC
irrh
oti
c A
LDC
irrh
oti
c A
LDC
irrh
oti
c A
LDD
onor
Donor
Donor
Donor
Pearson’s Correlation
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HCC Pathogenesis
Aravalli (2008) Hepatology: 2049
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MicroRNA Targets
Chen WJG 2009 p1665
LIVER
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miRNA Clinical
Outcomes
Junfang et al NEJM 2009 p1437
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Pearson’s Correlation
• Segregation is based on grade and cause of injury
• Donor < Low fibrosis < Severe fibrosis/Cirrhotic < HCC
• HCV vs ALD
Donor
Donor
Donor
G3
F0
Expla
nt
G1
F4
G3
F3
G1
F4
Expla
nt
G3
HC
C
Expla
nt
G1
F4
Expla
nt
G1
F4
Expla
nt
G3
F4
G1
F3
Expla
nt
G1
F4
Expla
nt
G3
F4
Expla
nt
G3
F4
Expla
nt
G1
HC
CExpla
nt
G3
H
CC
Expla
nt
G3
H
CC
Expla
nt
G1
HC
C
Expla
nt
G1
HC
CExpla
nt
G3
F4
ALD
ALD
ALD
Expla
nt
G3
HC
CA
LD
Expla
nt
G1
HC
C
Donor
G1
F0
G1
F0
G3
F2
G3
F1
G3
F2
G1
F4
G1
F0
G3
F1
G1
F4
G3
F3
Donor
Low Fibrosis
Severe fibrosis/ Cirrhotic
HCC ALD
miRNA profiling of HCV-induced fibrosis, cirrhosis and HCC
Hierarchal Clustering
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Personalised Medicine
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Interleukin-28B
1671 Patients from IDEAL
19q13.13
Rapidly confirmed• Australia Group• Japanese Group• European Group
Ge et al Nature 2009 461, p1
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IL-28B
Ge et al Nature 2009 461, p1
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IL-28B
Ge et al Nature 2009 461, p1
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New Technologies
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Sequencing costs
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Deep Sequencing Technologies
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Summary
• Genomics methods have already lead to personalized medicine– Warfarin therapy– Hepatitis C Treatment responses– Malignancy
• Deep Sequencing presents a “deluge” of data– Promise of personalised medicine– Analysis problems dramatically amplified