lab meeting outline

try and make new figure showing location among screen hits of the hypoxia hits with oe v 2 on x axis and 2 v 1 on y axis. emphasize that the majority of the transcription factors are not in inducing conditions when screened, whereas the hypoxia genes are.

you should also see where cell cycle genes fall and see if you can add this to the plot.

note: this will require you go through the images and say for sure which ones are expressed.

data on Rox1-YFP in HEM1::LEU2 background. Contrast with previous results. Explain that the major difference between two experiments was 1) plating OD 2) normalized time in imaging media. I conclude that the previously observed noise characteristics may be due to observing Rox1-YFP expression at different stages in progression to heme-starvation.

I also observe that ROX1-YFP is expressed at much lower level in HEM1::LEU2 background than in HEM1+ background and that the range of expression over which ROX1 expression can be controlled is small (~2-fold).

making deoxygenated culture tubes

add 10ml YPD + 2% glucose to vial

add 100ul Resazurin

cap the tubes--make sure to get the petroleum stopper top a bit wet so that when you close the screw top on it, it can rotate and fully tighten down.

bring water bath to rolling boil

place 9 tubes in boiling water for 10m

while tubes are still hot, sparge with N2, while bring tubes down to rt with water bath.

wait until following day to see if any tubes are leaking (based on indicator color change clear-> pink

protocol for ROX1-YFP induction

put 1 matchhead cells from freshly streaked cells into 100ul

put 50ul of this into 10ml deoxygenated media and allow to grow at 30' for ~20h

prepare microscopy plate (treat with conA). place plate on microscope and remove media from target wells. (note: it takes about 5m for the plate to stop moving once you put it on the plate-holder--you should have it stop moving before adding cells, so you can take high quality images immediately after plating.)

take 2ml and spin down in microcentrifuge tube. resuspend in SDComplete.

plate and wait ~1-2m until cells adhere to slide at appropriate density.

begin time-course

rox1 expression coordinated with ribosomal protein genes

McKnight lab has shown that when you grow yeast to saturation, then starve them for > 4h, then allow them to grow in a chemostat (constant glucose supply; dilution rate 0.1 hours ^-1; limited oxygen), they undergo metabolic cycling. HAP1 expression seems to correlate with O2 levels and ROX1 expression seems to peak just after peak of dO2 (dissolved oxygen).

Note that they only see metabolic cycles in two prototrophic strains, and not in lab strains BY and W303.

cheap high-throughput DNA sequencing is going to change the world in a shocking way (by 2020, I think). at present, it's not crystal clear how; there's a lot of nonsense speculation being thrown around and it's not easy to separate the legit potential applications from the bogus. After all, there have been many new technologies in biology that have been touted as the technology that will cure disease.
For example, microarrays were supposed to inform differences in gene expression (i.e. how much of a given protein is being made from a given gene) between, for example, a normal tissue sample and a tumor sample, and thereby identify the cause(s) of cancer. The bogusness: 1) this reasoning requires that cancer is caused by change in gene expression (dubious), and 2) that there would be sufficient statistical power to distinguish such a change from the intrinsic measurement error (equally dubious).

More common diseases, like cancer, are thought to be caused by mutations in several genes, and finding the causes was the principal goal of the $3 billion human genome project. To that end, medical geneticists have invested heavily over the last eight years in an alluring shortcut.

But the shortcut was based on a premise that is turning out to be incorrect. Scientists thought the mutations that caused common diseases would themselves be common. So they first identified the common mutations in the human population in a $100 million project called the HapMap. Then they compared patients’ genomes with those of healthy genomes. The comparisons relied on ingenious devices called SNP chips, which scan just a tiny portion of the genome. (SNP, pronounced “snip,” stands for single nucleotide polymorphism.) These projects, called genome-wide association studies, each cost around $10 million or more.

The results of this costly international exercise have been disappointing. About 2,000 sites on the human genome have been statistically linked with various diseases, but in many cases the sites are not inside working genes, suggesting there may be some conceptual flaw in the statistics. And in most diseases the culprit DNA was linked to only a small portion of all the cases of the disease. It seemed that natural selection has weeded out any disease-causing mutation before it becomes common.

The finding implies that common diseases, surprisingly, are caused by rare, not common, mutations. In the last few months, researchers have begun to conclude that a new approach is needed, one based on decoding the entire genome of patients.

It's funny how the story is told. The author suggests that the HapMap project was intended to definitively determine the causes of cancer. But it is common knowledge that disease is determined not only by genotype and but also by environment--a pair of identical twins share the same genotype, but do not always share the same diseases.

The Illumina/Solexa technology requires only 1 g of DNA per library, enabling the study of primary tumour DNA rather than requiring the use of tumour cell lines, which may contain genetic changes and adaptations required for immortalization and maintenance in tissue culture conditions.

Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression.

The main observation here is that negative autoregulation in a TF reduces variation in expression of a downstream gene. The thing I initially found curious about the data is that the no feedback (NF) system displays bi-modality. I imagined that, even if a downstream gene has two modes of expression, with a relatively stable protein, you wouldn't see two distinct steady states. They explain this by invoking slow promoter dynamics. These dynamics would have to be slow indeed seeing that a cell with an active YFP (in the expression mode) that switches to inactive, would take many generations to have reporter expression go back to the no expression mode. I think it's more likely that they are not observing expression at equilibrium, but are instead looking at an induction/no induction population. They grow the cells in CSM Glucose overnight and then in CSM Galactose for 16h.

There's more good stuff in this paper.