Lecture #2 - Yeast Genetics Flashcards
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
Synthetic Genetic Array - Interpreting the interactions Example
WT colony (AB) –> growth is 1
Mutant colony b (Ab) –> growth is 0.75 (slight growth defect)
Mutant colony a (aB) –> growth is 0.5 (bigger growth defcet)
To get the predicted size of the double mutants –> we multiply the sizes of the individual mutants –> 0.75 X 0.5 –> predict the double mutant to have a 0.375 growth size IF there is no intercation
- Uses multiplicative modeling –> model assumes that these two mutants are indepentdent BUT if the two genes are involved in the same pathway then the actual growth of the double mutant will be a different value
-
Positive Vs. negatuve Interactinos
In exmaple - predicted growth rate of double mutant is 0.375
IF the double mutant grows better than 0.375 then there is a positive interaction
- Positive interactions may mean that the genes are in the same pathway
IF the double mutant grows worse than 0.375 –> then there is a negative interaction
IF the double mutants growth rate is the product of the two single mutant growth rates = there is no interaction
IF the double mutant is dead (growth rate is 0) = the interaction is syntehtic leathl
Synthetic Genetic Array - Interpreting the interactions Example #2
You are interested in the function of Gene X and decide to look at how it interacts with other genes. Your ∆x strain growth rate is 0.5 compared to WT. You cross this with a deletion collection of 5,000 nonessential genes. Shown are 6 of these data points.
Analyzing Synthetic Genetic Array - Graph #1
Image:
Positive interaction = green
Negative inteaction = Red
No intreaction = Black
Gene 1 = has 5,000 data points (1 for each double mutant)
- Do this for every gene (Ex. Gene 2 also have 5,000 data points) –> computer will show the pattern and compare the pattern of 1 gene to all other patterns of other genes and find the ones that are more similar
- Doing an unsupervised cluster analysis
Analyzing Synthetic Genetic Array - Bigger Picture
Image – every row is 1 gene and it is show next to the gene that has the most similar pattern of interaction
- NOT looking at the gene in order 1-5000 INSTAED places genes next to each other than have the most similar pattern of interactions
Analyzing Synthetic Genetic Array - Bigger Picture (Zoom in)
Image:
3 genes at the top –> shows that the analysis puts genes next to each other because they have similar patterns
- Image – the first 3 genes have similar patterns and are likely to have similar functions
Negative genetic interactions = red –> shows
synthetic leathity on a large scale
If we know the function of gene 467 and gene 7 then now know the function of 1273 because it is likley a SU
Synthetic Genetic Array finding genes with similar functions
Genes of similar functions are found in SGA because when you delete 1 gene and there is a gene that functions to that gene it will show a similar pattern of interactions
Example – delete the alpha SU then that won’t be able to form a dimer OR delete the beta SU then you also can’t form the dimer = have similar genetic interactions with other genes –> alpha and beta tubulin would be next to each other on the map
What can’t be found in SGA
Because looking at deletions of genes you won’t find proteins but you can find parraell pathways
Red genes (negative interactions) = show parrallel pathways
What does it mean to have a positive interactions in SGA
Positive interactions report on protein complexes and gene networks without knowing anything about gene function
Example 6 SU complex (6 genes that makes 6 proteins in 1 complex)
- IF you remove any gene then you lose the function of the complex (Need all 6 genes for the complex to work)
IF remove 1 gene = cells grow at half of the rate vs. IF you delete a different SU then the cells grow at 0.5
IF you delete both SU (double mutant) you expect growth to be 0.25 (0.5 X 0.5) if there is no genetic interactions BUT when you have a double mutant you actualy get 0.5
- Get 0.5 because each mutant will remove the function of the whole complex = when remove the SU you will always grow at 0.5 (doesn’t matter id you delete 1 or 2 SU)
- Since expected 0.25 but grows at 0.5 = observed growth rate is higher = positive interaction
Where does gene X (gene 4037 fit in?)
Gene X likley functions with genes that have similar patterns (functions with 405 etc)
Gene X has positive intercation with some of the genes in the columns it might have functinos with those (could be in the same complex)
SGA with drugs instead of KO
For SGA – don’t need to only do KO mutants
Instead of making double mutants –> NOW look at how combining a drug on WT cells vs. Mutants
Take WT and add drug –> grow at 0.5
Take Drug and add to each of the single mutant –> look at growth
- Already know what the rate of growth of the single mutant without the drug is
END - get genetic pattern for teh drug –> have it cliuster
- Look at the patterns from the drug and see what genes it clusters in from the SGA analysis
Example - say the genetic pattern of the drug clusters with genes 301, 405, and 2894 –> The genes that it clusters with are good candidates for the genes/proteins that are being targeted by the drug
Is SGA only done in Yeast?
SGA is not limited to yeast – can also do in mammalian cells
In mammalian cells NOT mating cells to make double mutants BUT instead have 1 gene that is repressed or KO because of CRIPSR or shRNA
- Look at phenotype of cells that have 1 gene repressed or KO and then also do that for double mutant (viruses that will code to knocking out double mutants)
- Can do this in cultured cells
TheCellMap
Example of a gene network created based on SGA data
- Each single mutant will have a different set of results, or signature, based on its growth scores with the 5000 non-essential mutants
- The growth signature of each gene, cross-referenced with every other gene, will be different –> These mutants are then subject to cluster analysis to learn more about gene functions
- Clustering is based on negative and positive interactions –> if a and b negatively interact AND b and c negatively interact, then all three genes are likely functionally related
Can’t lose it plasmid yeast screen - Process
Start with a plasmid that has he WT gene of interest and a URA3 marker –> transform this plasmid into the yeast strain that has the mutation/deletion in the gene of interst –> NOW the cell will be phenotypicallt normal bevyase they have a WT copy of the gene of interst on the plasmid –> Once tranform the cel –> mutaginize the yeast with EMS to cause point mutations across the genome –> plate the mutogenized yeast onto non-slecetive medium so that eveyrthing will grow –> To find the synthetic leath genes –> replica plate onto 5-FOA plates
- 5-FOA = chemical that turns into a toxic metabolite when URA3 gene is present –> cells thay have the plasmid will die on 5-FOA
END – colonies that have synthetic lethal interactions will grow on non-selective meidum but NOT 5-FOA
- Once we have these colonies we can map their mutations to find the genes that are synthetic lethal with our gene of interest
CHECK THIS
Because have URA3 on plasmid –> cells that keep the plasmid have synthetic lethal –> Cells have to keep the plasmid = the cells grow on 5-FOA
- Looking for synthetically lethal mutant in the cell that can grow
Why does the Can’t lose it plasmid yeast screen work?
When yeast cells are not placed under selection –> the yeast will lose the plasmidsBUT cells that have synthetic lethal mutations MUST maintain the plasmid in order to grow
Results - Synthetic lethal mutant will grow on non-selective mediuam and not on 5-FOA because it must retain the plasmid carrying the URA3 gene
Can’t lose it plasmid – bacteria screen
Can perform a similar screen in bacteria to identify synthetic lethal interactions
Start - we have a deletion or mutant cell and a WT copy of this gene of interest on a plasmid (plasmid also has amplicilin resistence + LacZ reproter) –> THEN preform transposon mutogensis –> a kanamycin resistent gene is insterted randonly through the genome –> TEHn screen the bacteria on kanamycin + IPTG + X-gal plates
End – Bacteria that inserted a transposon will be able to grow on kanalycin and IPTG will cause the LacZ from the plasmid to be expressed –> LAcZ will metabolize the X-Gal = make blue product
- In cases where the transposon insertion and gene of interst are synthetic lethal + THEN the cell must mainatin the plasmid which has the LacZ gene –> colony truns blue
Comparative Transposon Sequencing bacteria screen Example
Example – reads mapping back to each gene (image)
Gene B is synthetic lethal to our gene of interest because the cells with a transposon that interrupts gene B can only grow in Wt but not in the mutant cells
Mechanism of synthetic lethal interactions - Parallel pathways
Synthetic lethal interacts can arise in genes in parrallel pathways
Example - redundant genes
- In the WT – both pathways function
- In mutant a - red pathway does not fucntion BUT gene b can funcion
- In mutant b – the blue pathway does not function BUT gene a can function so the cell survives
- The cell needs at least 1 of these pathways to survive –> SO if you mutate both pathways the cell dies
When you see synthetic lethality –> tells you 2 genes function in the same pathway
Mechanism of synthetic lethal interactions - Physically interacting proteins
Mainly seen in non-deletion mutants
If have a mutation in gene a –> then protein can still bind to protein b (get complex but it functions less effcicetley) ; If have a mutation in gene b –> then protein can still bind to protein BUT when you mutate both a and b (both interacting oartners are mutanted) then you can’t make a complex and the cells can’t grow –> cell dies
- Synthetic interaction tells you there are interacting proteins
- In image - when mutaate a or mutate b –> the change from circle the square still allows for intercation and function BUT when both a and b are mutated in the same cell the interaction is compelley lost
How do you isolate temperature sensative mutants
Purpose – Temperature sensatice mutant librarieres to permit study of essential processes
Want to study all 1,000 essential genes = need to generate a lot of mutants
To make the temperature sensitive mutant libraries = select for mutants that fail to grow at 36 degrees
- Process – Mutagenize WT yeast on rich medium (YPD) at 25 degrees –> cells will grow to colinies –> replica plate using velva pad onto rich medium at 36 degrees –> Select for mutants that do not grow
- Don’t actualy know what the muation is at this point (just collect genes that you know have mutations in essential genes that you wnat to study)
Yeast DNA content
Interphase yeast cells have 1C DNA content when use flow and stain for DNA
S phase cells have 2C DNA content when use flow and stain for DNA
Example use of Supressor Screen
Have WT cells with WT red and WT blue –> do mutant hunt and find a temperature sensitive mutant in the red gene - YOU want to find other genes that function with the red gene –> do a supressor screen
To do supressor screen - Start with temperature sensative mutants that can’t grow at 36 degrees (starting with mutant cels) –> mutogenize the cells –> NOW look at cells that can grow at 36 degrees –> cells that grow at 36 degrees have a supresor if the first mutation
- Asking what restores the phenotype
- First mutation causes the cells to be unable to grow at 36 degrees THEN have another mutation that allows the cells to grow at 36 degeres
Intragenic Supressors - True revertants
True revertant –> The mutant nucleotide chnages back to the WT nucleotides
- Red gene has a mutatiion –> then have a true reveratnt
Example (in temperature sensitive mutants) – After revertant –> NOW WT sequence and can grow at 36 degrees
Intragenic Supressors - Second site mutaons
Second site mutation (image shows in blue) –> Second mutation in the same gene that restores the phenotype
- Can restore the WT phenotype by affecting splice sites or amino acid seqeunce
Example – salt bridge – get mutation that allows the protein to fold again
Extragenic supressors - Interaction Supressor
Allele specific for the mutant and the supressor
(In example - Blue mutant only works when have red triangle)
Does not rescue null mutations –> example – a mutaion in the blue protein would not suppress a KO of the red protein and vica versa
Extragenic supressors - Interaction Supressor exmaple
Example – WT red and blue proteins interact with each other (in temoature sensative example to grow at 36 degrees you need the square to bind to the blue square to form a complex )
- Mutate Red = blue and red can’t interact = cells can’t grow at 36 degrees
- Blue protein can have an allele specific conformational mutations that allows red and blue to interact again –> restores the WT = cells grow at 36 degrees
- Mutant allele of the blue protein = the supressor
Bypass supressor - Example #1
Red protein is a maltose channel and the blue protein is a lacotse chanel
- When the red chanel is mutated –> maltose can’t be transproted
- When there is a bypass supressr in the blue lactose chanel –> allows the chanel to move BOTH maltose and lactose (NOW the functinoal red protein is no longer needed for the WT phenotype)
Start –> Mutate maltose = cells can’t grow on matose –> Find supressor in the mutates and now they can grow on maltose
Epistatic Supressor - Example
Red gene makes a circle that typically supresses the blue protein –> give WT phenotype
Blue protein normally prevents the cell from growing (Ex. Blue has a checkpoint function – when it is active it blocks the cycle and cells won’t grow)
When have Red = blocks the blue – cells grow
Mutate Red = Not blocking blue = blue is active = cells don’t grow (mutant phenotype)
Mutant blue (Blue and Red are both mutated) = Blue is not active = cells grow because you remove the checkpoint = restores WT phenotoe
Dosage Supressor - Example
Start – blue and red are equally expressed (Red and blue proteins need to be in equal balance)
In the mutant –> less red is made –> the unbalance amount of blue protein causes a mutant phenotye
THEN A compensatory muations in the blue = restores the stoicheometry of red:blue proteins –> brings balance back t the system –> get WT phenotype –> cells grow
- Blue = supressor
Mass Action Supressor - Example
Example – Blue and red proteins normally interact with each other (circle and square need to form a complex for the cells to grow)
Have mutation –> causes a conformational change which causes red protein to be misfoloded –> blue and red can’t interact –> cells don’t grow BUT those mutated red traingles are always going between triangle and circle in equillibrium –> over experess blue square = increase binding of the blue to the porperly folded red circle and stabilityzes the blue bindinig to the red = protein can fold and fucntion
- Overexpress blue protein = restores WT phenotype
- Blue mutation that leads to over expression = supressor muation
Example #2 – overexpress alpha tubulin = can form the alpha-beta tubulin dimer
Supressor screens in bacteria
START - we have mutant that will NOT grow when plated on galactose
Can grow up mutant in LB (rich media) –> plate on selective medium (which contains galactose) –> then see if the colonies with suppressor mutations will form
