20.109(S07): Screen for phenotypes, isolate RNA

Introduction

Life is evidently more complicated than base-pair chemistry since even a perfect cataloging of an organism’s genetic information does not allow us to build a cell from scratch. Furthermore, two cells in a multicellular organism can have identical genomes but different physiologies. Even at an organismal level, we see that identical twins do not exhibit identical traits. Moreover, minimal differences in DNA code can separate species. With human DNA sequence less than 1.25% different from that of chimpanzees, it appears too simple to believe, “…our fate is in our genes" (a phrase that was popularized when Nobelist Jim Watson told TIME magazine in 1989, "We used to believe our destiny was in the stars; now we know in large measure our fate is in our genes.”)

Rather than genetic content it may be changing expression patterns of a genome that explain cellular differentiation and development. A single cell develops into a differentiated multicellular organism by varying gene expression in groups of cells as they divide. A liver cell must express the parts of the genome related to liver function, while a skin cell uses the parts of the code for making skin related proteins. Scientists are trying to describe other “-omes,” such as the “transcriptome” (the complete RNA content of a cell or organism) and the “proteome” (its total protein content), to complement the cataloging of an organism’s total DNA content. Fortunately, techniques for detecting the RNA and protein output of a cell abound. Older methods have been used productively for decades and newer techniques offer increased sensitivity and higher throughput methods. Because of their widespread use, several fundamental techniques in gene expression analysis will be considered in detail.

One classic technique for monitoring gene expression is Northern analysis. In this approach, an RNA sample is electrophoresed through a polyacrylamide matrix and then transferred (“blotted”) from the gel to a solid support, usually made of nitrocellulose or nylon. The blot is then probed with radiolabelled DNA, or less often RNA, and then exposed to X-ray film. Hybridization of the probe to the blot is detected as a darkened area of the film and the signal gives information about the size and concentration of that RNA in the original sample. Valid Northern analysis data includes re-probing the blot with a “loading control” to demonstrate that each sample was equally loaded on the agarose gel and evenly transferred to the blot. Typical loading controls are 18S rRNA and actin mRNA since these are abundant transcripts in most cell types and are seldom affected by experimental conditions.

Sample Northern gel

An alternative to Northern analysis is q-PCR (quantitative-Polymerase Chain Reaction, sometimes also called RT-PCR which can stand for either Reverse Transcriptase-PCR or real time-PCR ... RT RT-PCR??). You gained experience with “end point PCR” at the start of this experiemental module when you used the final product of that amplification to transform yeast. With q-PCR, quantitative information is gleaned from the early stages of the PCR cycling protocol. A specialized thermal cycler is used as well as a fluorescent dye to monitor the amount of double stranded DNA in each reaction at each step of the PCR protocol. RNA is isolated from cells of interest (as you will do today) and converted to DNA using an enzyme called reverse transcriptase (as you will do next time). This DNA serves as the template in the q-PCR reactions. After a limited number of PCR cycles, the amount of PCR product can be sensitively detected by its fluorescence and quantitatively reflects the amount of transcript in the original sample.

A third means of measuring RNA in a cell is through a DNA microarray. In this technique the RNA from two cell types are simultaneously hybridized to DNA probes on a slide. The DNA probes represent some or all of the genome, enabling the relative amounts of gene expression for each gene in both cells to be assessed. This is the technique you will perform next time, and more details will be presented then.

Today we will experimentally address two related questions. First, we'll ask if losing a SAGA-subunit changes the yeast's physiology, looking for phenotypes associated with the mutation you've directed into the yeast genome. Phenotypic differences may reveal aspects of gene regulation and protein function related to the SAGA-complex and for the subunit you've chosen to study. Second, we'll consider each gene that's expressed in the wild type and the mutated cells, isolating RNA today so next time you might identify mRNAs whose production is affected when the SAGA-subunit you've selected is deleted. Both experiments will be informative, but it's important to note that neither can determine if, mechanistically speaking, the observed changes are a direct or indirect consequence of the mutated gene.

Protocols

Part 1: Agarose gel of PCR products

You will share an agarose gel with one other group.

1. Retrieve your PCR samples from the teaching faculty.
2. Move 10 ul of each sample to a labeled eppendorf tube.
3. Add 2 ul of loading dye to each of the eppendorf tubes.
4. Load these aliquots onto a 1% Agarose Gel (1xTAE), according to the following table.

Lane	Sample	Volume to load
1	100 bp Marker	5 ul
2	GROUP 1: PCR product/FY2068 template	~12ul
3	GROUP 1: PCR product/candidate A	~12ul
4	GROUP 1: PCR product/candidate B	~12 ul
5	GROUP 1: PCR product/candidate C	~12 ul
6	100 bp Marker	5 ul
7	GROUP 2: PCR product/FY2068 template	~12ul
8	GROUP 2: PCR product/candidate A	~12ul
9	GROUP 2: PCR product/candidate B	~12 ul
10	GROUP 2: PCR product/candidate C	~12 ul

The gel will run at 125V for approximately 45 minutes, and one of the teaching faculty will photograph it for you.

Part 2: Spot test candidates for phenotypes

The yeast you are working with, S. cerevisiae, was the first eukaryotic organism to have its genome fully sequenced.Goffeau et al, Science 1996 A full 10 years later there is still ongoing discussion and work on how to best annotate the genome. Fish et al, Yeast 2006 There is general agreement that S. cerevisiae genes number ~6000, and of these approximately 20% are essential. The essential genes also show noteable conservation, with homologs identified in other sequenced organisms. Those essential genes that have no human homologs are useful antifungal drug targets. How to best make sense of the other 4000 annotated genes? About 15% of these affect the cell's physiology in at least one of several discernable ways. In many cases, the function of these genes can be revealed by the phenotype that arises from their loss.

Many phenotypes have been described for S. cerevisiae. A very fine review of these was published by Michael Hampsey in Yeast 1997. He groups the phenotypes into the following categories:

Category	example of phenotype	Assay
Conditional growth	e.g. TS (37°) or CS (14°)	growth on rich media at restrictive condition
Cell cycle defect	e.g. large, unbudded cells or very few unbudded cells	microscopic examination
Mating and sporulation defect	e.g. failure to produce mating factor	e.g. halo assay
Auxotrophies, carbon catabolite repression, nitrogen utilization	e.g. Snf phenotype (Snf = "sucrose non-fermenter")	growth on incomplete or alternative media
Cell morphology and wall defect	e.g. bud localization	e.g. calcofluor staining
Stress response defect	e.g. sensitivity to heat shock	e.g. incubate 1 hr at 55° then test for viability
Sensitivities	e.g. canavanine	growth in presence of analog, antibiotic or drug
Carbohydrate and lipid biosynthesis defects	e.g. nystatin	growth in presence of synthesis inhibitor
Nucleic acid metabolism defects	e.g. UV light sensitivity	growth in presence of damaging agent
Other	e.g. caffeine	growth in presence of...

Part 3: Isolate RNA

DONE!