12. Lecture WS 2003/04Bioinformatics III1 Pharmacogenomics Pharmacogenetics is an old discipline....

12. Lecture WS 2003/04

Bioinformatics III 1

Pharmacogenomics

Pharmacogenetics is an old discipline.

One many distinguish pharmacogenetics (the study of a single gene) and

pharmacogenomics (study of many genes or entire genomes)

or use pharmagenomics for approaches that go beyond DNA to include mRNA and

proteins

Today, it is possible to assess entire pathways that might be relevant to disease or

to drug response at the DNA, mRNA and protein levels.

Eventually, the entire genome, transcriptome and proteome will be available.

Therefore, parmacogenetics/-genomics and disease genetics/genomic are

undergoing similar transitions, with a shift in focus from Mendelian examples (one

gene one disease) to more complex modes of genetic causation.



Where do drugs interact with proteins?

This figure shows the paths that are taken by the anti-epileptic drug phenytoin and the angiotensin-converting enzyme (ACE) inhibitor imidapril in the human body. Phenytoin is absorbed into the bloodstream at the gut and circulated through the liver to the brain. It crosses the blood–brain barrier where it binds and inhibits its target, neuronal sodium channels. It is pumped back out across the blood–brain barrier into the bloodstream by multidrug resistance protein 1 (MDR1 , also known as ABCB1) efflux pumps. At the liver, phenytoin is metabolized by the cytochrome P450 enzymes CYP2C9 and CYP2C19, and it is eliminated through the kidneys. Imidapril is a PRO-DRUG . After its absorption from the gut into the bloodstream it is hydroxylated in the liver to the active metabolite imidaprilat. Imidaprilat binds and inhibits ACE in the plasma. Imidaprilat is also eliminated through the kidneys.

Goldstein et al. Nature Rev. Gen. 4, 937 (2003)

http://us.expasy.org/cgi-bin/niceprot.pl?P08183





These associations werecompiled from the literatureby using the keywords„pharmacogenetics“ ORpharmacogenomcis“,„association study“ AND„drug response“,„polymorphism“ AND„drug response“.

Therefore, the list omitsmany polymorphisms andprobably includes somefalse positives.

Most of the polymorphismsare either in the drug targetor in a protein that is in thepathway in which the target acts.




The SNP Consortium




Haplotypes

The diagram shows 5 haplotypes. 12 SNPs are localized in order along the chromosome. The letters on the top indicate groups of SNPs that have perfect pairwise linkage disequilibrium (LD) with one another, and the numbers on the bottom indicate each of the 12 SNPs. SNP 9 is the causal variant, which in this simple example determines drug response: allele C results in a therapeutic response, whereas allele G results in an adverse reaction. In this example, the selection of just one SNP from each of the groups A–E would be sufficient to fully represent all of the haplotype diversity. Each haplotype can be identified by just five tagging SNPs (tSNPs), and the causal variant would be tagged even if it were not itself typed. So, tSNP profiles that are highlighted predict an adverse reaction to the medicine. Normally, LD patterns are not so clear-cut and statistical methods are required to select appropriate sets of tSNPs.




Haplotypes

b The diagram depicts the same 12 SNPs, but with different associations among them, as might happen in a different population group. Because patterns of LD are different, some patients would be misclassified if the same five tSNPs were used and interpreted in the same way. Using the same SNP profiles as defined in population A, haplotype profiles 1, 2 and 3 are predicted to have allele C at the causal SNP 9 (a therapeutic response), whereas haplotype profiles 4 and 5 are predicted to have an adverse response. However, because the pattern of association has changed, the new haplotypes 6 and 7 are misclassified as haplotype patterns 6 and 7 in population B.




Discovering genotypes underlying phenotypes: from mendelian diseases to complex diseases

Botstein & Risch, Nature Gen. 33, 228 (2003)

Traditional view: over the past decade, about 1200 genes causing human diseases or

traits have been identified, largely by positional cloning.

Identification of the gene knowledge of relevant protein(s) often leads to

understanding of the molecular and physiological basis of the disease phenotype.

Successful examples in positional cloning: identifcation of genes underlying

chronic granulomatous disease

X-linked muscular dystrophies

cystic fibrosis

Fanconi anemia

ataxia telangiectasia

neurofibromatosis I

Huntington disease

identification of genes underlying hereditary predispositions to

cancer, including retinoblastoma

breast cancer

polyposis colorectal cancer



Linkage mapping


Positional cloning begins with linkage analysis.

Families in which the disease phenotype segregates are analyzed using a

group of DNA polymorphisms.

Ideal method for diseases with very clear diagnosis. The limit of resolution

remains the number of meioses in which crossovers might have occurred.

In favorable cases (such as cystic fibrosis), the patterns of crossovers in the

region of the gene among the cohorts studied leaves only a few predicted

genes, all within about 1cM (~1Mb) as likely candidates.

In less favorable cases, there may be as many as a few hundred predicted

genes that might be the relevant disease genes.



Linkage disequilibrium


Greater power in fine-mapping is obtained by haplotype analysis, in which all

markers are considered simultaneously as haplotypes rather than individually.

Haplotype analysis allows the inference of likely historical crossover points,

which localize the disease mutation.

New algorithms based on haplotype analysis are being developed to estimate

statistically the likely locations of such crossovers and thus the likely location of

the disease mutation.

The success of linkage disequilibrium (LD) mapping depends heavily on the

degree of genetic heterogeneity underlying a disease sample.

Unless one or a few mutations account for most instances of disease, the

signal will be too inconsistent to find mutations.

Some degree of heterogeneity is tolerable and can be overcome by clustering

of disease chromosomes.



Lessons from cloned mendelian genes

HGMD lists 27.000 mutations in

1222 genes associated with

human diseases and traits.

In-frame amino acid

substitutions are the most

frequent.

Less than 1% are found in

regulatory regions.


These data provide overwhelming support for the notion that mendelian clinical

phenotypes are associated primarily with alterations in the normal coding

sequence of proteins.



Criteria for amino acid replacements

Distinguish

(1) biochemical severity of missense changes, and

(2) location and/or context of the altered amino acid in the protein sequence.

A useful guide is the Grantham scale:

categorize codon replacements into classes of increasing chemical dissimilarity

between the encoded amino acids:

conservative

moderately conservative

moderately radical

radical

„stop“ or nonsense.

There is a clear relationship between the severity of amino acid replacement

and the likelihood of clinical observation.




Clinical severity increases with severity of AA substitution

Purple bars represent the ratio of frequencies

of the indicated class of change compared to

conservative changes for functional human

genes compared to pseudogenes.

Orange bars represent the ratio of the

likelihood of clinical observation for a

conservative change versus the indicated

class of change.

A nonsense change is 9 times more likely to

present clinically than a conservative amino

acid substitution.

For the other changes, the ratios are 3, 2.3,

and 1.8.


9 x

The same trend exists for the relative abundance of the different types of substitutions

found in SNPs from human genes as compared with their abundance in pseudogenes.

Evolution selects against radical changes!



Clinical significance correlates with degree of cross-species evolutionary conservation

An obvious way to measure the

importance of a particular amino acid:

conservation across species.

The figure shows that the disease

probability decreases monotonically

with the number of amino acid

differences among species.

In simple terms:

if evolution allows mutations between

species, this amino acid cannot be so

crucial.


Relative risks (log odds ratios) for theobserved versus the expected number of amino acid changes.Purple: severe diseases, Orange: milder disease mutations (G6PD).



Correlation of clinical severity and severity of gene lesion

In numerous cases, genotype-phenotype correlation has identified milder forms of

disease that are associated with less severe mutations.

A classic example is Duchenne (severe) and Becker (mild) muscular dystrophy:

Duchenne is caused primarily by frame-shift deletions,

Becker is cause by in-frame changes.

Other examples:

hemolytic anemia – associated with globin mutations

hemochromatosis – high penetrance radical amino acid substitution

low penetrance milder amino acid substitution

Gaucher disease – common milder mutation associated with

fewer clinical symptoms

G6PD deficiency – severity of amino acid substitution correlates

with clinical significance




The future: understand complex diseases

Classical linkage analysis and positional cloning remain the methods of choice for identifying

rare, high-risk, disease-associated mutations, owing to their clear inheritance patterns.

Knowledge of Human genome sequence will certainly help.

But „simple“ mendelian inheritance is often not so simple:

- multiple different mutations are often identified in the same or in different loci,

with variable phenotypic effects and highly variable associated risks.

- mutational or genotypic heterogeneity can explain some of the clinical variability observed in

single-gene diseases, but usually not all modifier genes, environmental contributors.

For non-mendelian diseases and for diseases with multi-gene effects, all contributing loci

might be thought of as „modifiers“ as no single locus of large effect exists.




large-scale SNP discovery projects

Two strategies: „map-based“ or „sequence-based“.

It is unclear which one will be more effective.

The private sequencing effort has reported 2.1 million SNPs (Venter et al. 2001)

and the public SNP consortium has identified 1.4 million SNPs (Sachidanandam et

al. 2001).

Rates of false-positives (10-15%) are modest.

Rates of false-negatives (undetected SNPs) are more problematic.

Neither collection was based on the sequences of many individuals

many lower-fequency (< 10%) SNPs were not detected, especially those that are

specific to a single population.




fine-scale SNP discovery projects

Study A analyzed 313 genes (720 kb of genomic sequence) for 84 ethnically

diverse individuals.

Only 2% (or 6% excluding singletons) of the SNPs identified are in dbSNP

suggesting that there exist many more SNPs than the roughly 1.2 million

unique SNPs in dbSNP

Study B analyzed 65% of the unique sequence of chromosome 21 for 10

individuals.

36.000 SNPs were identified > 6.4 million SNPs for whole genome.

Only 45% of the SNPs in dbSNP were found in this study.

Conclusion: the number of SNPs in the human genome (defined by a rare-allele

frequency of 1% or greater in at least one population) is likely to be > 15 million.

Note: there are only 30.000 genes.




fine-scale SNP discovery projects

The alternative strategy to „map-based“ is based on genes and sequence. Here, genotyping

focuses on SNPs identified in coding regions that alter or terminate amino acid sequence, or

disrupt splice sites, or occur in promoter regions.

The table shows that we expect 50.000 – 100.000 such gene-related SNPs.

Based on results from cloned mendelian disease, one can prioritize amino acid replacements

according to (a) the severity of the alteration, and (b) the degree of evolutionary conservation.




Can disease-associated alleles be predicted from sequence?

Main feature that distinguishes a map-based approach from a genome-based

approach to genome-wide association studies is:

degree to which functional variants can be predicted on the basis of sequence in,

for example, coding and/or conserved regions of the genome.

Table 1 showed that – for mendelian phenotypes - most diseases are the result of

changes that cause loss or alterations in encoded proteins.

< 1% of listed mutations occur in regulatory regions (these would be more difficult

to predict from sequence).

The greatest risk of a disease phenotype is associated with splice-site mutations,

deletions and insertions.




Can disease-associated alleles be predicted from sequence?

Can this distribution of risks be extrapolated to alleles of moderate to low relative

risk – which are assumed to underlie complex disease phenotypes?


Literature: 18 changes – 15 AA substitutions, 1 large deletion, 1 frameshift, 1

variation in promoter region. This is not very different from high risk diseases

and is also biased to substitutions.



Natural variation in human membrane transporter genes:identify evolutionary and functional constraints

Large-scale SNPs and Haplotype maps have only analyed 24-40 chromosomes

within an ethnic population and therefore identified common variants (> 5%) with

good accuracy.

These screens could not identify less common variants that may have more

severe functional consequences.

Little is known about the relative levels of genetic diversity within classes of

genes.

Here: focus on membrane transporters which are important drug targets.

Leabman et al. PNAS 100, 5896 (2003)



Structure of Membrane Transporters

Predicted secondary structures

of two representative membrane

transporters from the ABC and

SLC superfamilies. The

transmembrane topology is

schematically rendered.Leabman et al. PNAS 100, 5896 (2003)

Transmembrane helices (25 residue long

stretches, purely hydrophobic; prediction

accuracy > 90%).

Typically 12-14 TM helices align to form

pore. External domains are very variable

in size.



Membrane Transporters

Membrane transporters play critical role in many biological processes:

- maintain cellular and organismal homeostasis by importing nutrients essential

for cellular metabolism

- export cellular waste products and toxic componds.

- important in drug response – they provide the targets for many commonly used

drugs

- are major determinants for drug absorption, distribution, and elimination.

Two major subfamilies

- ABC (ATP-binding cassette) transporters

- SLC (solute carrier transporters) – take up neurotransmitters, nutrients, heavy

metals ...

Here: screen for variation in a set of 24 genes encoding membrane transporters.




24 TM transporters with potential roles in drug response

Transporters are grouped based

on transporter family (e.g., OCT1,

OCT2, and OCT3 belong to the

SLC6 family; CNT1 and CNT2

belong to the SLC28 family).

Blue ovals: transporters of SLC

superfamily;

red rectangles, ABC superfamily;

green hexagon, P-type ATPase.

Typical substrates for each family

of transporters are listed. The

direction of transport is indicated

by an arrow pointing into the cell

(influx) or out of the cell (efflux).




Aims of SNP scan

Analyze 247 DNA samples of ethnically diverse collection (100 European

Americans, 100 African Americans, 30 Asians, 10 Mexicans, 7 Pacific Islanders).

Identify SNPs.

Aim 1: determine the levels and patterns of genetic diversity

- in different ethnic groups

- in different transporter families

- across different structural regions of membrane transporters.

Aim 2: combine population-genetic and phylogenetic analysis to identify amino acid

residues and protein domains that may be important for human fitness.

Infer functional consequences of amino acid substitution.

To identify polymorphisms, screen all exons plus 35 -100 bp of flanking intronic

sequence.




Variation in transporter genes

680 biallelic SNPs, 2 tri-allelic SNPs.

91/477 SNPs were already deposited in dbSNP.




Population specificity

421/680 SNPs are population specific.

248/421 are singletons = occur only once among 494 chromosomes.(This explains why large-scale SNP projects have sofar identified far less SNPs).

Of the 259 population-unspecific SNPs, 83 are present in all 5 populations.

Few population-specific alleles were found at high frequency:

only 4/278 African American-specific alleles had frequency > 0.1

only 1/50 Asian-specific allele had frequency > 0.1

The European American population sample had no population-specific allele

(0/80) at fequency > 0.05.

The relatively high incidence of moderately frequent population-specific alleles in

African Americans may facilitate identification of ethnic-specific disease loci in this

population.Leabman et al. PNAS 100, 5896 (2003)



Analysis of Nucleotide Diversity

On average, genetic variation in membrane transporters () is similar to that in

other genes.

Next: study nucleotide diversity in TM domains and in loop domains.




Variation across structural regions

As expected, amino acid

diversity (ns) is significantly

lower in TM domains than

in loops.

Consistent with observation

that TM domains are evolu-

tionary more conserved

than loops; suggesting that

there are constraints on TM

domains of transporters.


EC: evolutionary conservedEU: evolutionary unconserved

Agreement suggests that constraints on structural regions of proteins (e.g. TM

domains) occurs across long and short evolutionary distances for this set of

proteins.



ABC and SLC superfamilies

ABC and SLC superfamilies of transporters have evolved to transport structurally

diverse biological molecules.

TMDs of both superfamilies contain residues and structural domains responsible for

substrate specificity.

Only the loops of the ABC transporters contain ATP-binding domains.

Observation: is extremely low in TM domains of ABC transporters,

much lower than in TM domains of SLC family members.




Paralogue identification

Predicted secondary structures of two

representative membrane

transporters (BSEP and CNT1) from

the ABC and SLC superfamilies

showing positions of nonsynonymous

SNPs (leading to amino acid

mutations).

The transmembrane topology

schematic was rendered by using the

program TOPO.

Nonsynonymous amino acid changes

are shown in red.




Evolutionary conservation

Surprisingly, the extent of amino acid diversity did not parallel evolutionary

conservation:

the fraction of EU residues in the TM domains of the ABC superfamily is

significantly higher than in the TM domains of the SLC superfamily.

This implies that a protein segment (TM domains of ABC transporters) is more

constrained within humans than across species

may be related to substrate properties

_________________________________________________________________

For the SLC superfamily, NS-EC is significantly lower than NS-EU – both for the TM

domains and for the loops.

For the TM domains of the ABC superfamily, NS-EC ~ NS-EU. This may reflect

special functional demands on the TM domain of this superfamily.

Again: variation among humans does not always parallel phylogenetic variation!




Back to Pharmacogenomics

With the linkage of genomics with transcriptomics + proteomics,

pharmacogenomics is undergoing a similar shift in focus from Mendelian

examples to more complex modes of genetic causation.

Candidate genes for variable drug response:

(1) genes that code for drug-metabolizing enzymes (DME). Most DME-encoding

genes have polymorphisms that have been shown to influence enzymatic activity.

(2) proteins involved in drug transport.

Drug transporters (e.g. ABC and SLC) show considerable genetic variation

including many functional polymorphisms.




Future of Pharmacogenomics

To detect the effect of a gene variant that explains 5% of the total phenotypic

variation in a quantitative response to a drug by typing 100 independent SNPs

would require 500 patients to provide an 80% chance of detection assuming an

experiment-wide false-positive rate of 5%.

The behaviour of most drugs will be influenced by a wide range of gene products

(DMEs, transporters, targets, and others), and in many cases the importance of

polymorphisms in one of the relevant genes might depend on polymorphisms in

other genes.

As a simple example, CYP1A2 and N-acetyltransferase 2 act in different stages

in the pathway that metabolizes compounds in burnt meat.

Variants might interact to influence the risk of colorectal cancer.

The polymorphisms indicate that regulatory variants have a far more

important role in variable drug response than they do in Mendelian

diseases.


Date post:	19-Dec-2015
Category:	Documents
View:	225 times
Download:	0 times

12. Lecture WS 2003/04Bioinformatics III1 Pharmacogenomics Pharmacogenetics is an old discipline....

Documents