Lecture #2 - Yeast Genetics Flashcards
(93 cards)
Way to determine if two mutants are on the same gene
- Complementation analysis
- Test of function because the output is based on the phenotype
- Test of position –> Confirms the ressults of the complementation analysis
- Done using Likage Analysis
Complementation Analysis
Image – have 2 haploid yeast cells (each with a mutation)
- Red star = mutation
Mutant A – has a mutation in the green gene –> has the mutant phenotype
Mutant B – has a mutation in the purple gene –> has the mutant phenotype
BUT the diploid off spring of the two mutants has the WT phenotype because there is an intact copy of each gene
Recombination (overall)
Recombination = occurs during mitosis or meisois
Image – have 2 homologous chromosomes in a diploid cell (1 chromosome is red and 1 chromosome is blue)
- Have DNA replication –> THEN have a recombination event between homologous chrosome –> After recombination event one of the blue chromosomes now has a peice of red and one of the red chromosomes now has a peice of blue
- Represents a single recombination event
Linkage analysis is based on the concept of recombination
Recombination during meiosis
During meisosis 2 – each chromosome is separated into a single cell –> generates 4 haploid cels
IF1 1 recombination event occurs - Two of the haploid cells have a chromosomes that is identical to 1 of the parental haploids while the other two halpoud cells have recombinant chromosomes
How does recombination give us positional information about genes
Overall - 2 genes that are further apart or on separate chromosomes THEN they are less likely to recombine together to the same duaghter cell (more likeley to be inherited seperatley)
Example – cross over occurs between gene B (gene B is far from gene A)
- After 1 recombination event gene B is exchanged with 1 sister chromatid of each chromosomes
- Genes = UNLIKED (far away and recombination can occur)
Recombination between genes that are close together
2 genes that are close together = more likley to segregate together (NO recombination)
- It would be less likley for a recombination event to occur between genes A and B when A and B are close together
- Genes = considered linked
Overall - genes that are far apart are more likley to have recombination so the genes are unlinked ; genes close together are less likely to have recombination so they ate linked)
What are we looking for in linkage analysis
When we do linkage analysis in yeast we look for 3 different patterns:
- Tetratype
- Non-parental ditype
- Parental Ditype
Patterns = referred to as tetrads (tetrads represent a sinle miotic event)
The proportion of each of these segregation types can give us information on whether these genes are linked or unlinked
Tetratype
Occurs after a single recombination event
Includes 2 haploids with parental chromosomes + 2 haploids with recombinant chromosomes
Non-Parental ditype
Overall – have recombination event between both sets of chromosomes
- Less common than a tetratype because you require two recombination events around a specific region
- More distance between the mutants of interest the more likely there could be two recombination events
End – because you have recombination in both sets of chromosomes –> all 4 haploids have recombinant chromosomes
Parental Ditype
Parental ditype occurs when there are NO recombination events between chromosomes
END – have all 4 haploids with parental chromosomes
Tetrad segregation patterns in 2 linked genes
If there mutants are in the same gene THEN they are linked –> recombination is therefore unlikely because the mutations are close together
- When linked = you should observe more parental ditypes than tetratypes and more tetratypes than non-parental ditypes
- Linked genes = almost always on the same chromosome
End - # of parental ditypes > # tetratypes > # of non-parental ditypes
Tetrad segregation patterns in 2 unlinked genes
IF genes are further away from each other –> recombination between them is more likeley
- Diferent genes = unlinked = recombination likley
- Unlinked can be on the same or different chromosomes
Challenge with unlinked genes being on the same or different chromosomes
Because unlinked can be on the same or different chromosomes –> makes it tricky to define one pattern that is always true for any set of unlinked genes
Is there a cut off for declaring somthing linked/unlinked
There is NO hard cut off for formally declaring something unlinked
Generally genes on the same chromosome will follow an intermediate pattern that makes them look somewhat linked
IF we consider only genes on different chromsomes then we can say that the number of parental ditypes will be similar to the number of non-parental ditypes and that thee will be very few tetratypes
Synthetic Lethality
The interaction between two non-lethal mutations that results in cell inviability (double mutant is dead)
- Usually the mutants are in different genes
Example:
1. Have a normal haploid cell with the WT phenotypes (grows cell)
2. If there is a mutation (a) –> results in a red cell
3. Different Mutaions (b) –> results in a smaller cell
Mutation in a is viabile AND mutation in b is viable (cell just grows worse) BUT mutation in a and b in the same haloid cell is not viable (Cell does not grow at all)
Use of Synthetic interactions
Synthetic interactions = helps us find genes that are interacting with the gene of interest
Synthetic interaction can include synthetic lethality BUT it does not have to be
- When the viable double mutant just had reduced growth = called synthetic interaction
How do you isolate the cells that are dead in synthetic lethal interactions
Issue = IF you make a double mutant and the double mutant cells are dead = can’t isolate them
- Example I f you have mutant a and you want to find muatnts in gene b that are syntehtically lethal –> if you make that mutations the cells will be dead = can’t actually make that mutation
Solution – Use a sectoring Assay Yeast Screen
Sectoring Assay Yeast Screen
Uses the adenosine syntehsis pathway
Mutant ADE2 gene –> cells become red because they accumulate AIR
IF have a mutation in ADX genes (ADE4 or 7 etc) –> cells are white (can’t make red product)
Mutation in AD2 and ADX –> Cells are white
Can use the color phenotype to find cells with mutation that is synthetically lethal with Tub 1
- Can see double mutant based on color
Example Sectoring Assay Yeast Screen
- Start - genome of the yeast cell is AD2 and AD3 mutant –> cells should be white
- THESE cells ALSO have mutation of interst (Mutation in Tub2)
- Cells ALSO have a plasmid that codes for WT AD3 and WT Tub2 –> cells become red because AD3 in plasmid
If just grow cells with plasmid at 25 degrees –> don’t need to keep the plasmid = they will lose the plasmid = the cell turns white
- Mutant cells grow at 25 degrees = they don’t need the WT Tub2 from the plasmid = cells lose the plasmid = cells become white (get white sectors)
NOW - Can take the mutant cells at 25 degrees –> mutagenize the cells (NOW get lwT new mutant) –> look for cells that can’t lose the plasmid (stay red)-> Means that the cells have mutants that are synthetically lethal
- When you mutagenize the cells again = they aquire a new mutation (lwT) that is synthetically lethal with Tub 2 mutation –> if had thsoe muations toegetrh teh cells die SO if those cells lose the plasmid they are dead = INSTEAD they keep the plasmid to keep the WT Tub2 to be able to grow –> Because keep TUB2 = also keep AD3 gene = keeps the cells red
- Look for cells that need the Tub2 gene = have the ADE3 gene = cell are red
IF have synthetic lethality with Tub2 –> NOW if the cells lose the plasmid they die –> Cells will keep the plasmid –> Cells have the Tub2 gene + the ADE3 gene –> Cells stay red –> pick the red colonies (will have mutants in them that will be synthetically lethal)
Synthetic Genetic Array (overall)
Method used to screen for synthetic interactions between all gene combinations
Purpose - finds interactions of genes across the genome and cluster these interactions based on function
- Finds synthetic intreactions (lethal and non-lethal)
Genome scale + unbiased + Don’t need to know anything about the gene that you study
Overall - Screen 5,000 non-essential genes and observe growth defects
- All possible double mutants are made
- We can’t screen for essential genes in SGA because the mutants are not alive
Why Synthetic Genetic Array useful
- Genes with the same or similar function share phenotypes –> cluster analysis reveals genes involoved in same celular prcesses
- Find genes with related function to the gene of intrest - 2 – Genes showing string positive interactions often code for SU of a protein complex
Synthetic Genetic Array - Process
Using yeast deletion collection construct haploid double mutants –> quantitatively asses growth phenotype of single and double mutants –> Identify positive or negative interactions
- Diameter of the colony is to get the average growth
- Make 5,000 KO of the non-essential genes (delete 1 gene at a time) using HR
- Each strain has a deletion in 1 gene
To make haploid double mutants - Mate two mating types where 1 has gene 1 deleted and one has gene 2 deleted –> get haploid strain with mutation in gene 1 and gene 2
End – make all possible combinations of double mutants of the 5000 genes –> measure how the double mutants grow and compare to growth of single mutants
Synthetic Genetic Array - Acquiring Data
To get data – plate double mutants in rows and columns
Image:
Columns is genes 1-50 ; Rows = other gene that you test against
Top left – gene 1 KO and gene 200 KO –> look at the growth rate of the double mutant
- NEED to know growth of gene 1 KO on its own and gene 200 KO on its own
Zoom in image –
Say looking at gene 10 KO with gene 1 or KO with gene 11 –> see growth in some double mutants but also have a space with no growth (have double mutants where the cells don’t grow)
- Measure diameter for all of the combinations
Synthetic Genetic Array - Interpreting the interactions
Overall - need to compare the single mutants size to the double mutant size
Measure growth of single mutants and all combinations of the double mutants –> assign a growth score –> use multiplicative model to determine what we expect