Genetic Technologies I should also acknowledge in advance all the people whose teaching material I’ve pinched off the web for these talks. http://www.stats.gla.ac.uk/~paulj/tech_genetics.ppt.

Genetic Technologies I should also acknowledge in advance all the people whose teaching material I’ve pinched off the web for these talks.

Overview Why learn about genetic technologies?
The molecular geneticist’s toolkit Genetic markers Microarray assays Telomeres RNA interference (RNAi) This talk covers a very large field, so the aim is not to give a detailed understanding of particular techniques, but first to understand the basic tools that lie behind the techniques used to generate molecular genetic data, and then to illustrate them by giving examples of the most common techniques.

Why learn about genetic technologies?
In general, anyone doing data analysis would agree that it’s important to understand the data we’re analysing. This is obviously true whether we’re analysing blood pressures, the amount people smoke, blood cholesterol levels or genotypes. We obviously need to know how data is produced before we can even decide what kind of analysis is appropriate. However, a more detailed level of knowledge can also help us as assess the data’s reliability, and how errors might affect the conclusions of our analysis. The aim of this talk is to understand the processes behind the generation of genetic data. Not so much the biological processes, because its assumed that we’ll understand these before we plan and carry our the data analysis, but the laboratory processes.

We need to understand the processes that generated the data Understanding of biology obviously necessary Understanding of lab techniques will enhance our ability to assess data reliability Errors in any measurement can lead to loss of power or bias Some genetic analyses are particularly sensitive to error because they depend on the level of identity between DNA sequences shared by relatives the more data is collected, the greater the chance of false differences Before we can analyse data we need to understand the process that generated it

Individual Genotype A 177, 179 B 179, 179 If we take this example of two rows of genotype data. These data come from microsatellite markers, which I’ll introduce properly later but for the moment we just need to know that they record the length of a particular bit of DNA. Here we can see two individuals, A and B, with different genotypes, A has etc. We might be interested in this marker because we know that, in a particular family, allele 177 is close on the genome to a disease allele, so we’re using this microsatellite locus as a marker of this disease allele. What is the probability that the observed genotype is wrong? Is this probability the same for all observed genotypes? What impact will a realistic range of errors have on power?

The molecular geneticist’s toolkit

Most genetic technologies are based on four properties of DNA
DNA can be cut at specific sites (motifs) by restriction enzymes Different lengths of DNA can be size-separated by gel electrophoresis A single strand of DNA will stick to its complement (hybridisation) DNA can copied by a polymerase enzyme DNA sequencing Polymerase chain reaction (PCR) Some of these properties apply to RNA as well

DNA can be cut at specific sites (motifs) by an enzyme
Restriction enzymes cut double-stranded DNA at specific sequences (motifs) E.g. the enzyme Sau3AI cuts at the sequence GATC Most recognition sites are palindromes: e.g. the reverse complement of GATC is GATC Restriction enzymes evolved as defence against foreign DNA Sau3AI GATC CTAG

ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA Let’s say we have a sample that consists of a short length of DNA that we happen to be interested in because it’s involved in causing a disease. We might have obtained this piece of DNA by the polymerase chain reaction, which I won’t go into just now but we’ll come back to that later.

Sau3AI GATC CTAG ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA

ACTGTCGATGTCGTCGTCGTAGCTGCT GATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAG CATCGATCGA Sau3AI GATC CTAG

ACTGTCGATGTCGTCGTCGTAGCTGCT TGACAGCTACAGCAGCAGCATCGACGACTAG GATCGTAGCTAGCT CATCGATCGA ACTGTCGATGTCGTCGTCGTAGCTGCTGA TGACAGCTACAGCAGCAGCATCGACGACT TCGTAGCTAGCT AGCATCGATCGA We’ll see why this is useful in a minutbut first we need to km]now about another technique

Different lengths of DNA can be separated by gel electrophoresis
DNA is negatively charged and will move through a gel matrix towards a positive electrode Shorter lengths move faster You can’t actually see the DNA while it’s running through the gel. Generally the DNA will be made visible after the gel has finished either by a DNA specific stain, or because it had previously been marked with a radioactive or fluorescent label. Another very useful property of this technique is that once a piece of DNA has been separated on a gel, it can be recovered unharmed for further analysis, and can even be inserted into a living organism (often a bacterium).

Slow: 41 bp ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA Medium: 27 bp ACTGTCGATGTCGTCGTCGTAGCTGCT TGACAGCTACAGCAGCAGCATCGACGACTAG Fast: 10 bp GATCGTAGCTAGCT CATCGATCGA F M S

Recessive disease allele D is cut by Sma3AI: ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA Healthy H allele is not cut: ACTGTCGATGTCGTCGTCGTAGCTGCTGAGCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTCGCATCGATCGA F M S HH HD DD

M S HH HD DD

A single strand of DNA will stick to its complement
ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA

Fragment frequency Flourescence This property is useful because we almost always want to focus on a particular part or parts of the human genome, for example a putative disease gene, or telomeres. If we look back at our earlier example where we were analysing a short fragment of DNA that we got by the polymerase chain reaction that carried a disease-linked mutation. PCR is one way of looking at a particular bit of DNA, and it depends on this property of specific hybridisation. An earlier method, one that is still in use, is Southern blotting, which we’ll look at now. We don’t start out from the point of having a convenient manageable piece of DNA that we’re interested in, we have whole chromosomes, which are too big to practically separate with a gel. We can cut them up using a restriction enzyme, but if we run that through a gel Fragment length in bp

Southern blotting (named after Ed Southern)

This technique allows you pick out specific DNA sequences. It’s main drawbacks are that it’s slow and therefore expensive, and requires a fairly large amount of DNA.

DNA can copied by a polymerase enzyme
ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA

ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA A G T G C A A G C T DNA polymerase G G A A G A G T T C T C C C A G T A A G

ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA A G T G C A A G C T DNA polymerase G G A A G A Now lets say we add to the mix a small number of bases that are modified in two ways: (1) they cause the chain to stop growing and (2) each letter has a separate dye attached. G T T C T C C C A G T A A G

ACTGTCGATGTCGT

ACTGT ACTGTCGAT ACTGTCGATGT ACTGTCGATGTCGT ACTGTCGATGTCGTCGT ACTGTCGATGTCGTCGTCGT ACTGTCGATGTCGTCGTCGTAGCT ACTGTCGATGTCGTCGTCGTAGCTGCT ACTGTCGATGTCGTCGTCGTAGCTGCTGAT ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGT ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCT ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT

ACTGTCGATGT ACTGTCGATG ACTGTCGAT ACTGTCGA ACTGTCG ACTGTC ACTGT T G A C Time Fluorescence T C G A T G T etc Fluorescence Time

Add original paper

Polymerase chain reaction (PCR) A method for producing large (and therefore analysable) quantities of a specific region of DNA from tiny quantities PCR works by doubling the quantity of the target sequence through repeated cycles of separation and synthesis of DNA strands

A thermal cycler (PCR machine) G, A, C, T bases Forward primer Reverse primer G Heat resistant DNA polymerase A DNA template C T

Like southern blotting, PCR allows you to examine a single specific site on the genome. In southern blotting you get the specificity from the bind of the probe to the template. In PCR the job of the probe is done by the primers, which pick out specific sequences flanking the area of interest. One of the main advantages of PCR is that you only need a tiny amount of DNA for each reaction, so you do 100s or 1000s of assays on a single sample, whereas southern blots require relatively quantities. E.g. a blood sample might have enough DNA for one blot but 1000s of PCRs. PCR is also much easier to automate and scale up, and therefore much cheaper.

One of the

In the words of its inventor, Kary Mullis… PCR can generate 100 billion copies from a single DNA molecule in an afternoon PCR is easy to execute The DNA sample can be pure, or it can be a minute part of an extremely complex mixture of biological materials The DNA may come from a hospital tissue specimen a single human hair a drop of dried blood at the scene of a crime the tissues of a mummified brain a 40,000-year-old wooly mammoth frozen in a glacier. In the words of its inventor, PCR can… This list is interesting because the quality of the data you get out of PCR depends very much on the quality and quantity of DNA that goes in. Very high error rates can be associated with using low quality and quantity DNA, although it’s less well appreciated that certain kinds of errors can result from using too much DNA in a PCR reaction.

The invention of PCR had a huge impact on genetics, and won the nobel prize in Chemistry for its inventor, the rather eccentric Kary Mullis.

Specific DNA-cutting restriction enzymes DNA size separation by gel electrophoresis Hybridisation using labelled DNA probes Synthesis of DNA using DNA polymerase (PCR) (and ligase for re-joining cut ends) With these tools we construct most genetic assays.

Genetic markers The first class of techniques I’m going to talk about is genetic markers.

Genetic markers What are they? Variable sites in the genome
What are their uses? Finding disease genes Testing/estimating relationships Studying population differences What do they do?

Eye colour Phenotype Genotype Brown eyes BB or Bb Blue eyes bb
A genetic marker doesn’t have to be hi tech, and it doesn’t even have to be a direct assay of DNA. Any trait that is under strong genetic control could be used. You could use eye colour as a dominant marker.

ABO blood group Phenotype Genotype AB A AA or AO B BB or BO O OO
The ABO blood group has been used as a genetic marker for about 90 years. It is a partially dominant marker. Obviously eye colour and blood group have their disadvantages as genetic markers. Each is dominant to some extent, so some information is hidden. Each is also a single marker. If we want to search across the whole genome for a gene that contributes to disease, we need markers that are spread across the whole genome. They also don’t directly assay the DNA mutation behind the change, so there’s the possibility firstly that we’re missing some of the underlying DNA variation, and that there isn’t a perfect correlation between genotype and phenotype (for example, what do we do with green eyes?).

The ideal genetic marker
Codominant High diversity Frequent across whole genome Easy to find Easy to assay So our shopping list for the perfect marker would look something like this:

Modern genetic markers: SNPs
SNPs are single nucleotide polymorphisms Usually biallelic, and one allele is usually rare Can be protein-coding or not This example is a T/G SNP. An individual can be TT, TG, GG Healthy allele A is cut by Sma3AI: ACTGTCGATGTCGTCGTCGTAGCTGCTGATCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTAGCATCGATCGA Recessive disease B allele is not cut: ACTGTCGATGTCGTCGTCGTAGCTGCTGAGCGTAGCTAGCT TGACAGCTACAGCAGCAGCATCGACGACTCGCATCGATCGA They can have up to 4 alleles, but almost all of them have 2, simply because mutations are very rare and the chance of two occuring at the same base, and both reaching detectable frequency in the population is very small. W measure the diversity of a marker by probability of any two alleles chosen at random from the population being different.

Allele-specific oligonucleotide OLA: oligonucleotide ligation assay Clin Biochem Rev (2006) 27: 63–75

Most SNPs have very low diveristy

No error 2% error Common homozygote 960 940 Heterozygote 40 50 Rare homozygote 1 11 most disease alleles will be relatively rare

Clin Biochem Rev (2006) 27: 63–75

ARMS: amplification refractory mutation system or allele-specific PCR Clin Biochem Rev (2006) 27: 63–75

OLA: oligonucleotide ligation assay Clin Biochem Rev (2006) 27: 63–75

Molecular beacon probes or allele-specific PCR Clin Biochem Rev (2006) 27: 63–75

Pyrosequencing or allele-specific PCR Clin Biochem Rev (2006) 27: 63–75

Modern genetic markers: microsatellites
Microsatellites are short tandem repeats (STR, also SSR) Usually high diversity Usually not in protein coding sequence This example is an (AC)n repeat; a genotype is usually written n,n With k alleles there are k(k+1)/2 possible unordered genotypes ACTGTCGACACACACACACACGCTAGCT (AC)7 TGACAGCTGTGTGTGTGTGTGCGATCGA ACTGTCGACACACACACACACACGCTAGCT (AC)8 TGACAGCTGTGTGTGTGTGTGTGCGATCGA ACTGTCGACACACACACACACACACACGCTAGCT (AC)10 TGACAGCTGTGTGTGTGTGTGTGTGTGCGATCGA ACTGTCGACACACACACACACACACACACACGCTAGCT (AC)12 TGACAGCTGTGTGTGTGTGTGTGTGTGTGTGCGATCGA 7 8 9 12 7,7 7,8 8,8 7,9 8,9 9,9 7,12 8,12 9,12 12,12 Diversity is much higher in microsatellites as they usually have many alleles due to having much higher mutation rates. Diversity tends to be higher than 0.5 and is generally about 0.75.

Modern genetic markers: microsatellites

Microsatellites versus SNPs
Codominant Yes Diversity High Low Frequent 10,000s 3 million Easy to assay Easy to find No No, but… Other advantages of SNPs: they are very simple and they’re all pretty similar, which makes them easier to automate; having only 2 states reduces the possibility of error compared with microsatellites.

Uses of SNPs and microsatellites
The HapMap project has discovered millions of SNPs Their high density in the genome makes them ideal for association studies, where markers very close to disease genes are required Microsatellites More suitable for family-based studies, where high variation is valuable and lower levels of resolution are required Population association studies: need close markers because we’re looking for associations between disease genes and markers that have persisted over hundreds of generations Family: association just needs to survive one generation There is crossover SNPs may win as they become cheaper and faster to genotype

The molecular geneticist’s toolkit Genetic markers Microarrays Telomeres RNA interference (RNAi) This talk covers a very large field, so the aim is not to give a detailed understanding of particular techniques, but first to understand the basic tools that lie behind the techniques used to generate molecular genetic data, and then to illustrate them by giving examples of the most common techniques.

Specific DNA-cutting restriction enzymes DNA size separation by gel electrophoresis Hybridisation using labelled DNA probes Synthesis of DNA using DNA polymerase (PCR) (and ligase for re-joining cut ends) With these tools we construct most genetic assays.

The molecular geneticist’s toolkit Genetic markers Microarrays Telomeres RNA interference (RNAi) This talk covers a very large field, so the aim is not to give a detailed understanding of particular techniques, but first to understand the basic tools that lie behind the techniques used to generate molecular genetic data, and then to illustrate them by giving examples of the most common techniques.

Microarrays Microarrays are a way of telling which genes are switched on and to what degree…

Gene expression Transcription: DNA gene → mRNA in nucleus Translation:
mRNA → protein in cytoplasm Microarrays use mRNA as a marker of gene expression Nucleus Cytoplasm

What are microarrays? A microarray is a DNA “chip” which holds 1000s of different DNA sequences Each DNA sequence might represent a different gene Microarrays are useful for measuring differences in gene expression between two cell types They can also be used to study chromosomal aberrations in cancer cells

Principles behind microarray analysis
Almost every body cell contains all ~25,000 genes Only a fraction is switched on (expressed) at any time in any cell type Gene expression involves the production of specific messenger RNA (mRNA) Presence and quantity of mRNA can be detected by hybridisation to known RNA (or DNA) sequences …as a surrogate for the level of gene expression. Why would you want to know this?

What can microarray analysis tell us?
Which genes are involved in disease? drug response? Which genes are switched off/underexpressed? switched on/overexpressed? For example,

Before microarrays: northern blotting
Extract all the mRNA from a cell Size-separate it through a gel Measure level of expression using a probe made from your gene of interest

Northern blotting: still useful for single-gene studies

Microarray analysis: probe preparation

Microarray analysis: target preparation

Arthritis Research & Therapy 2006, 8:R100

Microarrays can be used to diagnose and stage tumours, and to find genes involved in tumorigenesis
Copy number changes are common in tumours Loss or duplication of a gene can be a critical stage in tumour development Frequency plot of copy number alterations nFeiggautrivee 1 tumors tions in ER positive and Frequency plot of copy number alterations in ER positive and negative tumors. The top two panels show the frequency of gains, indicated by the green bars ranging from 0 to 1, and losses, indicated by the red bars ranging from 0 to - 1, in 62 sporadic breast tumors for each clone. The bottom panel displays the magnitude of the t-statistic for each clone computed based on the smoothed data as described in the Methods. The horizontal dotted lines indicate the statistic cut-off corresponding to the FDR-adjusted p-value of 0.05 (blue) and 0.1 (green). Chromosome BMC Cancer 2006, 6:96

Problems of microarray analysis
Gene expression ≠ mRNA concentration Easy to do, difficult to interpret Standardisation between labs Lots of noise, lots of genes (parameters) e.g. p = 10,000 low sample size e.g. n = 3

Telomeres

Telomeres and telomerase
Telomeres are repetitive DNA sequences at the ends of chromosomes They protect the ends of the chromosome from DNA repair mechanisms In somatic cells they shorten at every cell division, leading to aging In germ cells they are re-synthesised by the enzyme telomerase Centromere Every time a cell multiplies to make two new cells, special zones at the ends of its chromosomes, called telomeres, become shorter. Once the telomeres reach a certain length, the cell stops dividing and eventually dies. The only cells to escape this fate are those that divide to make eggs and sperm. In these cells, a substance called telomerase builds the telomeres up again, so they remain the same length. Telomere

Why do we need telomeres?
At every cell division each chromosome must be replicated DNA is synthesised in one direction only The “lagging strand” is synthesised “backwards” in 100–200 bp chunks Spend some time on this. Be sure to mention leading and lagging strands.

Leading strand This isn’t a problem for the leading strand…

Lagging strand …but 100–200 bp of single stranded DNA are left hanging at the end of the lagging strand, and are lost. So at every cell division bases are lost.

Terminal (GGGTTA)n repeats buffer DNA loss

In germ cells, telomerase “rebuilds” telomeres

Health implications of telomere shortening: aging

Health implications of telomere shortening: cancer
Cancer tumour cells divide excessively, and will die unless they activate telomerase Telomerase activation is an important step in many cancer cell types Telomere length can be used to diagnose tumours Telomerase is a potential target of cancer therapy What role do telomeres play in cancer? As a cell begins to become cancerous, it divides more often, and its telomeres become very short. If its telomeres get too short, the cell may die. It can escape this fate by becoming a cancer cell and activating an enzyme called telomerase, which prevents the telomeres from getting even shorter. Studies have found shortened telomeres in many cancers, including pancreatic, bone, prostate, bladder, lung, kidney, and head and neck. Measuring telomerase may be a new way to detect cancer. If scientists can learn how to stop telomerase, they might be able to fight cancer by making cancer cells age and die. In one experiment, researchers blocked telomerase activity in human breast and prostate cancer cells growing in the laboratory, prompting the tumor cells to die. But there are risks. Blocking telomerase could impair fertility, wound healing, and production of blood cells and immune system cells.

Measuring telomeres Two principal methods
Southern blotting Quantitative PCR (qPCR)

Measuring telomeres TRF = telomere restriction fragment length

RNA interference (RNAi)
The first class of techniques I’m going to talk about is genetic markers.

What is RNAi? Generally genes are studied through the effects of knockout mutations in particular experimental organisms RNAi is a quick and easy technique for reducing gene function without the necessity of generating mutants that can be applied to any organism It has the potential to treat diseases caused by over-expression of genes The discoverers, Andrew Fire and Craig Mello, have just won the Nobel prize (announced 2 October).

Principles of RNA interference (RNAi)
Injection of double-stranded RNA (dsRNA) complementary to a gene silences gene expression by destruction of mRNA transcriptional silencing stopping protein synthesis Gene expression can be switched off in specific tissues or cells by the injection of specific dsRNA Takes advantage of endogenous defences against double-stranded RNA viruses??????

RNAi RNAi is a natural phenomenon, which probably evolved as a defence against dsRNA viruses, and is also involved in the regulation of gene expression.

Fascinating facts: each cell contains about 2 m of DNA, divided into 46 chromosomes (so each chromosome is on average 5 cm long, that means it’s 10,000 times longer than the width of the nucleus). Clearly this is an organisational problem.

Uses of RNAi Investigating role of genes by knocking down (not out) gene expression in specific tissues at specific developmental stages Potential use in gene therapy macular degeneration: two phase I trials currently under way therapies being developed for HIV, hepatitis, cancers

Limitations of RNAi Target specificity: how do you know the dsRNA isn’t interfering with other genes? Interpretation of results Risks for gene therapy Function isn’t knocked out, it’s reduced Knockdown may not reveal gene function Might not give therapeutic effect Gene therapy dead mice look up liver problem

Genetic Technologies I should also acknowledge in advance all the people whose teaching material I’ve pinched off the web for these talks. http://www.stats.gla.ac.uk/~paulj/tech_genetics.ppt.

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Genetic Technologies I should also acknowledge in advance all the people whose teaching material I’ve pinched off the web for these talks. http://www.stats.gla.ac.uk/~paulj/tech_genetics.ppt.

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Presentation on theme: "Genetic Technologies I should also acknowledge in advance all the people whose teaching material I’ve pinched off the web for these talks. http://www.stats.gla.ac.uk/~paulj/tech_genetics.ppt."— Presentation transcript:

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