Transcript Intro to Genomics & PM - Genomics & Bioinformatics at Purdue

An Introduction to Genomics, Pharmacogenomics,
and Personalized Medicine
Michael D. Kane, PhD
Associate Professor, University Faculty Scholar, Graduate Education Chair
Department of Computer and Information Technology
College of Technology
&
Lead Genomic Scientist, Bindley Bioscience Center at Discovery Park
Purdue University
West Lafayette, Indiana 47907
Bioinformatics.tech.purdue.edu
Introduction to Genomics
DNA is Information Storage
“Zipped Files”
Decompression
“Executable Files”
CAGGACCATGGAACTCAGCGTCCTCCTCTTCCTTGCACTCCTCACAGGACTCTTGCTACT
CCTGGTTCAGCGCCACCCTAACACCCATGACCGCCTCCCACCAGGGCCCCGCCCTCTG
CCCCTTTTGGGAAACCTTCTGCAGATGGATAGAAGAGGCCTACTCAAATCCTTTCTGAG
GTTCCGAGAGAAATATGGGGACGTCTTCACGGTACACCTGGGACCGAGGCCCGTGGTC
ATGCTGTGTGGAGTAGAGGCCATACGGGAGGCCCTTGTGGACAAGGCTGAGGCCTTCT
CTGGCCGGGGAAAAATCGCCATGGTCGACCCATTCTTCCGGGGATATGGTGTGATCTTT
GCCAATGGAAACCGCTGGAAGGTGCTTCGGCGATTCTCTGTGACCACTATGAGGGACTT
CGGGATGGGAAAGCGGAGTGTGGAGGAGCGGATTCAGGAGGAGGCTCAGTGTCTGAT
AGAGGAGCTTCGGAAATCCAAGGGGGCCCTCATGGACCCCACCTTCCTCTTCCAGTCC
ATTACCGCCAACATCATCTGCTCCATCGTCTTTGGAAAACGATTCCACTACCAAGATCAA
GAGTTCCTGAAGATGCTGAACTTGTTCTACCAGACTTTTTCACTCATCAGCTCTGTATTCG
GCCAGCTGTTTGAGCTCTTCTCTGGCTTCTTGAAATACTTTCCTGGGGCACACAGGCAA
GTTTACAAAAACCTGCAGGAAATCAATGCTTACATTGGCCACAGTGTGGAGAAGCACCG
TGAAACCCTGGACCCCAGCGCCCCCAAGGACCTCATCGACACCTACCTGCTCCACATG
GAAAAAGAGAAATCCAACGCACACAGTGAATTCAGCCACCAGAACCTCAACCTCAACA
CGCTCTCGCTCTTCTTTGCTGGCACTGAGACCACCAGCACCACTCTCCGCTACGGCTTC
CTGCTCATGCTCAAATACCCTCATGTTGCAGAGAGAGTCTACAGGGAGATTGAACAGGT
GATTGGCCCACATCGCCCTCCAGAGCTTCATGACCGAGCCAAAATGCCATACACAGAGG
CAGTCATCTATGAGATTCAGAGATTTTCCGACCTTCTCCCCATGGGTGTGCCCCACATTG
TCACCCAACACACCAGCTTCCGAGGGTACATCATCCCCAAGGACACAGAAGTATTTCTC
ATCCTGAGCACTGCTCTCCATGACCCACACTA
THEREDCAT_HSDKLSD_WASNOTHOTBUT_WKKNASDN
KSAOJ.ASDNALKS_WASWET_ASDFLKSDOFIJEIJKNAW
DFN_ANDMAD_WERN.JSNDFJN_YETSAD_MNSFDGPOIJ
D_BUTTHEFOX_SDKMFIDSJIR.JER_GOTWET_JSN.DFOI
AMNJNER_ANDATEHIM.
Start with a thin 2 x 4 lego block…
Add a 2 x 2 lego block…
Add a 2 x 3 lego block…
Add a 2 x 4 lego block…
organism
estimated size
estimated
gene
number
average gene density
chromo
-some
number
Homo sapiens
(human)
3200 million bases
~30,000
1 gene per 100,000 bases
46
Rattus norvegicus
(rat)
2750 million bases
~30,000
1 gene per 100,000 bases
42
Mus musculus
(mouse)
2500 million bases
~30,000
1 gene per 100,000 bases
40
Drosophila melanogaster
(fruit fly)
180 million bases
13,600
1 gene per 9,000 bases
8
Arabidopsis thaliana
(plant)
125 million bases
25,500
1 gene per 4000 bases
5
Caenorhabditis elegans
(roundworm)
97 million bases
19,100
1 gene per 5000 bases
6
Saccharomyces cerevisiae
(yeast)
12 million bases
6300
1 gene per 2000 bases
16
Escherichia coli
(bacteria)
4.7 million bases
3200
1 gene per 1400 bases
1
H. influenzae
(bacteria)
1.8 million bases
1700
1 gene per 1000 bases
1
The onion genome
is 6-times bigger
that the human
genome
The lily genome is
30-times bigger that
the human genome
Year
In 2008 a new gene sequence was uncovered every 1.7 seconds!
…equivalent to 483 DNA base pairs every second of every day!
GenBank Data
Base Pairs
Sequences
1982
680,338
606
1983
2,274,029
2,427
1984
3,368,765
4,175
1985
5,204,420
5,700
1986
9,615,371
9,978
1987
15,514,776
14,584
1988
23,800,000
20,579
1989
34,762,585
28,791
1990
49,179,285
39,533
1991
71,947,426
55,627
1992
101,008,486
78,608
1993
157,152,442
143,492
1994
217,102,462
215,273
1995
384,939,485
555,694
1996
651,972,984
1,021,211
1997
1,160,300,687
1,765,847
1998
2,008,761,784
2,837,897
1999
3,841,163,011
4,864,570
2000
11,101,066,288
10,106,023
2001
15,849,921,438
14,976,310
2002
28,507,990,166
22,318,883
2003
36,553,368,485
30,968,418
2004
44,575,745,176
40,604,319
2005
56,037,734,462
52,016,762
2006
69,019,290,705
64,893,747
2007
83,874,179,730
80,388,382
2008
99,116,431,942
98,868,465
DNA contains “Genes” (i.e. “blueprint for living systems on earth)
(
)
gene
(
)
gene
()
(
gene
)
gene
“Genes” are the ‘coding’ information to make “Proteins”
Proteins are the functional units of life…enzymes, structures, etc., etc., etc.,…
(i.e. the bricks, mortar, steel, hinges, cables, motors, etc.)
Example: Hemoglobin
Introduction to PharmacoGenomics
Single Nucleotide Polymorphisms (SNPs) are simple changes (or differences) in the DNA
sequence that appear to have little or no impact on human health. They represent 90% of
all human genetic variations.
Genetically similar to a mutation, but distinct in that a SNP is not causal to a clinical
disease or disorder (or at least not yet causally linked, and not really applicable to ages
>40 yrs old).
Across the human genome we average approximately 1 SNP for every 300 base pairs of
DNA (over one million known SNPs that occur at a frequency of 1% or higher in the world
population).
Important Consideration: Inheritance
The appearance of deleterious mutations during evolution tend to NOT be inherited for
obvious reasons, at least those that affect growth, reproduction and viability.
…and our modern existence is the result of millions of years of tolerated (and
occasionally beneficial) changes in our genome, which is most often evident in what we
can and cannot eat or consume (think: evolutionary pressure & natural selection)
Monomethyl Hydrazine (in “False” Morel Mushrooms)
(many examples of “toxins” in nature, many of them
are presumably synthesized to prevent consumption
or predation of the host plant or organism)
Modern drug discovery & development falls outside
the tolerances
& toxicity that
have resulted from
Tylenol: Acetaminophen
(Cats?)
evolution, because most of these compounds have
NEVER been seen in nature.
Introduction to PharmacoGenomics
When you ingest a drug, the drug is absorbed into the circulatory system and is
distributed throughout the body.
The drug is then available to carry out its intended ‘mechanism of action’ (MOA). In
the case of WARFARIN, it inhibits Vitamin K Epoxide Reductase Complex 1
(VKORC1), and reduces blood clotting. It is the largest selling anticoagulant in the
world, and the leading case in support of Personalized Medicine”.
Subsequently, the body has the ability to eliminate the drug from the body through
“drug metabolism”, which is primarily carried out in the liver. WARFARIN is
metabolized primarily by the oxidative liver enzyme CYP2C9, which basically adds
an oxygen group to the WARFARIN structure thereby inactivating its MOA and
increasing its likelihood of elimination from the body via the kidneys (urine).
For this reason, drug tests that utilize urine a sample source often look for the
“metabolite” of the drug in the urine, rather than the ingested drug.
IMPORTANT: If you are prescribed WARFARIN, you have a condition that generates
potentially life-threatening blood clots. If you are dosed with too much WARFARIN
you could die from complications due to internal bleeding, yet if you are dosed with
too little WARFARIN you may be in danger of serious consequences due to
circulating embolism.
The “ideal” dosing curve for WARFARIN
Drug Plasma Concentration vs. Time
Minimum toxic
plasma concentration
Minimum effective
plasma concentration
WARFARIN
MOA:
METABOLISM:
VKORC1 - Inhibition to prevent blood clotting
CYP2C9 – Removable from the body
What would happen if there was a SNP in the gene for VKORC1 that (1) did NOT
affect the clotting cascade, yet altered the protein enough to prevent WARFARIN
binding and inhibition?
The drug is present in the patient, but
NOT effective in patients that have
this specific SNP!
RESULT: Excessive blood clotting and
circulating emboli.
It is estimated that SNPs in VKORC1
are responsible for 15-30% of
variability in WARFARIN therapy.
WARFARIN
MOA:
METABOLISM:
VKORC1 - Inhibition to prevent blood clotting
CYP2C9 – Removable from the body
What would happen if there was a SNP in CYP2C9 that reduced the rate of drug
metabolism and elimination of WARFARIN?
The drug dosing curve would be
elevated due to decreased
metabolism and clearance of the
drug from the body.
RESULT: Increased risk of
complications due to internal
bleeding, associated with
WARFARIN overdosing.
There are 2 different SNPs in
CYP2C9 that decrease WARRAFIN
metabolism, occurring in 7% and
11% of the population, respectively.
Introduction to Personalized Medicine
It is estimated that up to 50% of variability in WARFARIN therapeutics and effectiveness
are due to the presence of genetic variations (SNPs) in the genome.
This is certainly true for most other prescription drugs on the market, in light of variability
that we all are familiar, such as decreased compliance, drug-drug interactions, certain
drugs are more effective in some people, etc.
PERSONALIZED MEDICINE: using clinical genotyping to identify which drugs (and drug
doses) are most safe and most effective in an individual, by identifying which SNPs that
patient harbors (if any) that can be used to predict the patient’s response to a prescribed
drug.
Missense mutations with functional effects
mapped in the crystal structure of human
CYP2C9 protein bound with warfarin (PDB:
10G5). S-warfarin and heme are shown in the
skeleton model with pink and red, respectively.
Amino acid residues are shown in the sphere
mode with colors.
Introduction to Personalized Medicine
APPLIED GENOMICS: Personalized Medicine vs. Diagnostics/Prognostics
Modern healthcare can utilize the DNA testing as a means to determine an
individual’s risk for developing certain diseases (i.e. Diagnostics and
Prognostics), but this use of clinical genotyping is associated with serious legal,
ethical and business hindrances.
GINA: The Genetic Information Non-discrimination Act (passed into law May 21,
2008, effective Nov 21st, 2009).
Personalized Medicine applies the methods of clinical genotyping ONLY to
genetic markers associated with drug safety and drug efficacy, these markers
are NOT associated with disease.
Furthermore, the practice of personalized medicine will significantly decrease
adverse drug responses in the population (one of the top ten causes of death in
the US), thereby making pharmacotherapeutics safer, and prevent the removal
of beneficial drugs from the market.
Therefore personalized medicine is supported by a viable ‘value proposition’ to
benefit pharmaceutical companies, healthcare insurers, and healthcare
consumers.