Genetics/Genomics in Hepatitis and Liver Disease

Title slide

Scott L. Friedman MD

Overview

• Definitions / basics of genetics, genomics &

epigenetics

• Why learn about genetics and genomics?

• Applications of genomics in liver disease

- HCV response to Alpha IF – IL28B SNP

- SNPs that predict fibrosis progression in HCV

- Pharmacogenomics and hepatic drug toxicity

• Summary / Predictions

Genetics and Genomics - Definitions

Genetics – focus on single genes and their effects

Genomics – refers to the action of all genes in an organism

- concerned with the structure, function, evolution and mapping of

genomes

DNA to RNA

RNA

to protein

5'

Unwound

DNA

tRNA

mRNA

5' 3' 3'

5'

Transcription

Translation

NH2

C O O H

Peptide

A A A A

The Basics – DNA to RNA to Protein

Typical Gene Organization

CCAAT box

TATA box

Silencer

Enhancer

-35 -10

3' 5'

Core promoter

Ex1 Ex2 Ex3

Transcriptional unit

Regulatory unit

mRNA: AAAA

Transcription

NH2 - Met - aa-aa-aa-aa . . . aa - COOH Protein:

6

RNA: AAAA

CAP

Alternative Gene Splicing 5’

Gene transcription

Exon 2

3’

Exon 1 Exon 3 Exon 4

Splice B Splice A

Exon 2 Exon 1 Exon 3 Exon 4 Exon 1 Exon 3 Exon 4

AAAA

5’

3’ RNA

Protein A Protein B

mRNA mRNA

Genetic Code

Universal

Redundant

Wobble - 3rd base

Start = AUG = Met

Stop = UAA, UAG, UGA

Genetic Code

Assessment of Disease Risk using

Single Nucleotide Polymorphisms (SNPs)

• Single Base Variants found in > 1% of population

• ~ 300,000 Common SNPS per Genome

• Useful for Gene Discovery for Complex Diseases

Pt. A: ...CTGATACAATGCATC

Pt. B: ...CGGATACCATGTATC

Pt. C: ...CTGATGCCATGCATC

Insights from the

Human Genome Project

Project began in 1990, completed in 13 years

Transfer information to public for discovery

Consider ethical, legal, & social implications (ESLI)

FINDINGS:

• Only 2% of genome is genes

• 98% is “junk” DNA

• Sequence is >98.5% identical in all humans

- Including across races/ethnic groups

Humans Have Many Fewer Genes than Expected – Why?

Organism No. Genes

Drosophila (fruit fly) 13,600

Arabidopsis (flowering plant) 25,500

Mouse 23,000

Humans? 22,000!

Humans have large regions that don’t code for genes:

- highly conserved regions (e.g., long non-coding (linc) RNA)

- alternative splicing, which generates multiple transcripts from

single genes

- epigenetic regulation may be far more important in humans

Definition:

Changes in gene

expression or

cellular phenotype

without affecting the

DNA sequence

Epigenetics

ENCODE –

The Next Step after the Human Genome Project

ENCODE – “Encyclopedia of human DNA elements”

Goal: to identify all regions of transcription, transcription factor

association, chromatin structure and histone modification in

the human genome sequence

• A large consortium of scientists to explore all components

that influence gene expression and phenotype

• Results published in > 30 papers in 2012

ENCODE Highlights

• 80% of genome contains elements linked to biochemical fxns

DNA variants linked to disease lie within or near non-coding fxnl DNA elements

75% of the genome is transcribed at some point in cells

Genes that are highly interlaced with overlapping transcripts are synthesized

from both DNA strands – this has implications for the definition of a ‘gene’

• Doubled the number of known recognition sites for DNA

binding proteins

• Chromosome loops & twists enable communication between

distal elements

Ecker et al, Nature 489, 52-55, 2012

Why Learn about Genetics and

Genomics?

• Understanding of disease pathogenesis

• Define risk of disease

• Disease classification/definition

– Prognostic implications

– Optimize treatment strategies - timing of OLTx, mechanism-specific therapy

• Prevention strategies (eg DILD, ALD)

• Design and personalize new treatments

• Determine risk to children & family members

Haemochromatosis Wilson’s Disease

Cystic Fibrosis PFIC & BRIC

Alagille’s Syndrome

a1-Antitrypsin Deficiency

Glycogen Storage Diseases

Hepatic Porphyrias

Simple Mendelian Disorders

Hepatitis B & C Infection

Non-alcoholic fatty liver

Alcoholic Liver Disease

Primary Biliary Cirrhosis

Primary Sclerosing Cholangitis

Autoimmune Hepatitis

Complex Traits

MacGregor et al. Trends in Genetics 16, 131(2000).

Disease Heritability (%)

Asthma 60

Blood pressure 40-70

Bone mineral density 60-80

Cervical and lumbar disc degeneration 60-80

Insulin-dependent diabetes (IDDM) 70

Obesity 50-90

Osteoarthritis 50-90

Rheumatoid arthritis 60

Ulcerative colitis 50

Common Complex Disease Have a

Significant Genetic Component

Genotype with SNPs in predicted but

uncharacterized genes

Whole Genome Scans

Genotype with SNPs in known genes to

accelerate disease association studies

Candidate Gene Approach

Finding Disease Genes and Variants

Finding the gene “hypothesis-free”

Whole genome SNP scanning

• Use SNP map to “scan” whole genome in cases and

controls

• >2.7 million SNPs in current database

• Requires high throughput genotyping technology

• Used to be restricted to pharma companies, but no

longer - especially at Mount Sinai!

Impact of Genomics on Hepatitis and

Liver Disease

• Response to alpha IF in HCV

• Genetic Risk of fibrosis progression

in HCV

• Pharmacogenomics and drug

toxicity

60 M

b

Chromosome 19

A Polymorphism on Chromosome 19 Predicts SVR: IL-28B

Polymorphism rs12979860

IL28B gene IFN Lambda-3 gene

3 k

b

19q13.13

Ge D, et al. Nature. 2009;461:399-401.

Chromosome 19 graphic courtesy of Oak Ridge National Laboratory. Available at:

http://www.ornl.gov/sci/techresources/meetings/ecr2/olsen.gif. Accessed on: October 21, 2009.

Ge et al, Nature, 2009

IL-28B CC genotype is associated

with SVR

Evolving Paradigms for Predicting Response to IFN-based

Rx for HCV – Genetics emerges!

1995-2000

• Genotype 2/3

• No advanced fibrosis

• Low viral load

• Younger age

• <40 years

• Female

• Weight

2007-2011

• Lack of steatosis

• No insulin resistance

• Adherence

• Rapid viral response (RVR)

• Ribavirin dosage

• Race/ethnicity

• IL-28B

• Anemia

McHutchison JG, et al. N Engl J Med. 2009. Manns MP, et al. Lancet. 2001

Patton HM, et al. J Hepatol. 2004; Poynard T, et al. Lancet. 1998.

2011-present

• Race/ethnicity

• low viral load

• absence of cirrhosis

• statin use

• IL-28B

• Genotype 1a/1b

• On treatment viral response

Lead-in

eRVR

Impact of Genomics on HCV Progression

Host, not Viral Factors Correlate with

Fibrosis Progression in HCV

• No correlation between either HCV viral load or genotype and fibrosis progression

• Gender, alcohol use, and other risks contribute, but don’t explain all the differences

• SNPs may correlate with fibrosis progression in humans

0

1

2

3

4

0 10 20 30 40 50

F METAVIR

Duration in years

Rapid

Intermediate

Slow fibroser

Poynard et al. Lancet 1997; 349: 825

N=1157

Patients with HCV have Variable

Fibrosis Progression

SNPs Associated with Cirrhosis Risk Score (CRS)

Huang H, et al. Hepatology, 2007

Predictors Public ID Gene (Chr) Risk

Genotype or

Phenotype

P valuea

(Univariate)

ORs (95%CI)

SNP1b AZIN1 (Chr8) GG 0.0002 3.23

(1.76-6.11)

SNP2 rs4986791 TLR4 (Chr9) CC 0.0004 3.11

(1.66-5.81)

SNP3 rs886277 TRPM5 (Chr11) CT, CC 0.0006 2.05

(1.36-3.08)

SNP4 rs2290351 none (Chr15)

242

AG,AA 0.0038 1.86

(1.22-2.82)

SNP5 rs4290029 none (Chr1) GG 0.0001 2.35

(1.52-3.63)

SNP6 rs17740066 none (Chr3) AG, AA 0.0014 2.76

(1.48-5.15)

SNP7 rs2878771 AQP2 (Chr12) GG 0.0003 2.17

(1.42-3.30)

HCV patients

Low

Medium

High

Equal Stratification of Risk in Placebo

and Rx Groups is Critical

• Low, medium and high refer to fibrosis stage or progression rate

Placebo Group

Rx Group

Unequal

stratification!

HCV patients

Low

Medium

High

Selection of Patients with High Fibrosis Progression Rate will Optimize

Antifibrotic Clinical Trials

• Low, medium and high refer to fibrosis progression rate

Placebo Group

Rx Group

SNPs Associated with Fibrosis Risk -

Implications

1. Potential to predict disease progression and

refine Rx

2. Possible correlation of liver fibrosis SNPs with

progression in other organs, including kidney,

lung, & pancreas

3. Discovery of new genes mechanistically linked to

pathogenesis

Using SNP information to Uncover New Biology

Huang H, et al. Hepatology, 2007

Predictors Public ID Gene (Chr) Risk

Genotype or

Phenotype

P valuea

(Univariate)

ORs (95%CI)

SNP1b AZIN1 (Chr8) GG 0.0002 3.23

(1.76-6.11)

SNP2 rs4986791 TLR4 (Chr9) CC 0.0004 3.11

(1.66-5.81)

SNP3 rs886277 TRPM5 (Chr11) CT, CC 0.0006 2.05

(1.36-3.08)

SNP4 rs2290351 none (Chr15)

242

AG,AA 0.0038 1.86

(1.22-2.82)

SNP5 rs4290029 none (Chr1) GG 0.0001 2.35

(1.52-3.63)

SNP6 rs17740066 none (Chr3) AG, AA 0.0014 2.76

(1.48-5.15)

SNP7 rs2878771 AQP2 (Chr12) GG 0.0003 2.17

(1.42-3.30)

Toll-Like Receptors (TLRs)

Beutler B. Nature 2004,430(6996):257-63.

• A family of mammalian

transmembrane pattern-recognition

receptors

• Recognize structural components

unique to bacteria, fungi and

viruses

• Signal and activate inflammatory

responses

LPS

TLR4

• Two well-known SNPs

correlate with SLOWER fibrosis

progression:

• Asp299Gly - “299”

• Thr399Ile - “399”

Prediction:

Genomics Will “Personalize” Health Care

What Is Personalized Medicine?

• Use medications and other

treatments that would work

best for each individual

• Avoid medications that would

cause an individual to have

adverse side effects

• Predict which illnesses an

individual was likely to get

and design a personalized

health care plan to prevent or

detect these diseases early

New York Times Magazine, Sunday, November 6, 2005.

Genetic Differences in Drug Metabolism The First Frontier of Personalized Medicine

“Pharmacogenomics”

• To determine efficacy and dosing of

drugs

• To predict risk of toxicity

Personalized Medicine -

Genetic Definition of Subpopulations

With Different Response Profiles

Drug Induced Liver Injury Network (DILIN)

https://dilin.dcri.duke.edu/

Summary & Predictions –

Genetics and Genomics in Hepatitis and Liver Disease

• Genomic information will profoundly improve our ability to predict:

Disease risk and progression rates

Response to therapies

Drug-related liver toxicities

• The demand for integrating genomic information will require:

‘Big data’ platforms to integrate genomic and EMR data

Software systems to insert decision-support guidance based on these data at

the point-of-care

• The need to assimilate these approaches will be driven by:

Insurers who seek to prevent disease and optimize Rx to reduce costs

Entrepreneurs and businesses with novel IT and technology solutions

Academic medical centers who can ‘ride the wave’ (e.g. Mount Sinai)

The public, and patients & their families, NOT by providers