4 posts tagged “yeast”

Steve Oliver
University of Cambridge
Keynote Talk 1
http://friendfeed.com/rooms/biosysbio
http://conferences.theiet.org/biosysbio/

It is not unexpected that Steve Oliver chose to talk about yeast, and calls it the perfect eukaryotic model organism. You can look at it in a coarse-grained, top-down approach. This approach includes research using Metabolic Control Analysis (MCA), which he calls a "shortcut to modelling metabolism". The central device of MCA is the Flux Control coefficient, which is a measure of the degree of control an enzyme has on a pathway flux.

You can change the concentration of gene products and measure the impact on flux. With this in mind, they have in the past created a deletion mutant for each of the protein coding genes in yeast. After insertion of the disruption cassette, you can sporulate and then perform tetrad dissection: if it is an essential gene, then there will not be any growth. If there is growth, it is non-essential and you have a colony containing the knockout.

You can look at the changing proportion of different mutant types in a population. They looked at competition between heterozygous and hemizygous mutants, and looked at the differences in growth rate. They found that, in pairwise comparison between the different condition, if they were haplosufficient in one condition they were sufficient in the others. The grape juice condition looked like nothing else: at the top of the list of haploinsufficient list had to do with the transport of pyrimidines. Haploproficiency (HP) is much more context/condition-dependent. Virtually every gene coding for the 26S proteasome were in the list for N-limited haploproficiency.

Haploinsufficient genes were vastly overrepresented in chromosome III irrespective of the type of limitation (except for one). It's the chromosome that determines the sex. The sexes are "a" and "alpha". There are three types: amphimixis (maximises chances of heterozygosity), haplo-selfing, and automixis/intratetrad mating. Haploinsufficient (HI) genes are more similar to their pre-duplication ancestors than their "ohnologs". The haploinsufficient gene is probably the ancestral version of the pair. K.lactis MAT chromosome III is also enriched for HI orthologs.

You can also do experiments which change the flux and measure products of gene action. This work was done in what his lab called "The Big Experiment", and makes use of a chemostat, from which we can get information about the transcriptome, metabolome, and proteome. These measure transcription changes that are wholly due to the change in flux. 493 genes were upregulated with increasing growth rates, and 398 genes were downregulated with increasing growth rates. Looking at the GO terms for those genes were were upregulated with growth rate include ribosome biogenesis and protein biosysthesis. Only 47 of the HI genes have their transcript levels under growth control, and only 26 of the HP types.

Is this a universal law, or context-dependent? It was found to be entirely context-dependent. Decided to repeat the composition experiments in a completely different environment: a turbidostat rather than a chemostat. In this case, there was no nutrient limitation. Looking at the HI genes in a turbidostat, they have the very functions that are to do with growth rate (note: I missed part of that point).

While the laws determining which genes control growth rate may change according to the selective conditions... (sorry!). The discovery of HP in nutrient-unconstrained conditions suggests that yeast has sacrificed short-term gain in favor of long-term survival.

Have worked with Ross King and the robot scientist work going on there. They've also started on a logical cell model encoded in Prolog. This is essentially a directed graph, with metabolites as nodes and enzymes as arcs. If a path can be found from the cell inputs to all the cell outputs, then the cell can grow.

They've created an experimental cycle that can be navigated by the robot, which can do some work on hypothesis inference. They removed the labels on the graph, and then used Abductive Logic Programming (ALP) to infer those missing arcs/labels in the metabolic graph. Abduction example follows. Rule: If a cell grows, then it can synthesise tryptophan. Fact: cell cannot grow. Therefore, the cell cannot synthesis tryp.

They tried a number of different experimental strategies (e.g. ALP versus naive versus random). Found that ALP was as good as graduate students who were presented with the same problem. They "closed the loop" wrt hypothesis formation, testing and validation. Original work was proof-of-principle as they got the robot to re-discover existing knowledge. They're now working on the discovering new knowledge.

To do this, they expanded the background knowledge of the robot, improved the efficiency of hypothesis generation, and extended the original qualitative methodology to allow for quantitative measurements. Basic premise was that growth should occur iff there was a path from growth medium to defined end end-points. The robot would then try to fill in the gaps in the model, where there must be enzymes etc to carry out specific steps. One strategy is to find yeast homologs of genes coding for proteins with appropriate EC numbers.

They have some new hardware to be their robot scientist, called Adam. It has the capacity to perform over 1000 experiments per day. You find that most of the genes that encode the enzyymes are often telomere-associated and have paralogs elsewhere in the genome. It's a very convoluted situation that was "unlikely to be solved by classical genetics." Keep an eye out for the upcoming Science paper called "The Automation of Science": the paper should appear in a couple of weeks.

Personal Comments: A great speaker and an interesting talk. Unfortunately wasn't able to capture absolutely everything he said...!

Please note that this post is merely my notes on the presentation. They are not guaranteed to be correct, and unless explicitly stated are not my opinions. They do not reflect the opinions of my employers. Any errors you can happily assume to be mine and no-one else's. Please let me know of any errors, and I'll fix them!

BBSRC Systems Biology Grantholder Workshop, University of Nottingham, 16 December 2008.

Alistair Brown presented the CRISP (University of Aberdeen and Imperial College London) approach to studying stress responses in fungal pathogens (Combinatorial Responses in Stress Pathways).

Survival is dependent upon environmental adaptation. How do cells respond to environmental stresses? Most studies focus on responses to a specific stress. Stress responses are relatively well-characterized in the benign model yeast, S. cerevisiae. However, in nature cells are simultaneously exposed to combinations of several stressees. Therefore, a better question is how do cells respond to combinatorial stresses? Their major focus is on Candida albicans and Candida glabrata. Normally a relatively harmless commensal for humans, but can cause problems. The two are quite far apart evolutionarily. Stress responses are important for infection (evidence from microarray exps that both organisms activate stress responses when come into contact with hosts). Their focussing further on osmotic stress, oxidative stress, and nitrosative stress. Individual stress responses are complex, dynamic, and nonlinear, and interactions may be cooperative or antagonistic. Interactions are also likely to be time-and dose-dependent. It's impractical to test all possible combinations experimentally, therefore the aim is to model combinatorial responses, and then use these models to select the most informative experiments.

First, model the individual stress responses, then combine these models. Approach is to identify components of each system based on our understanding of the corresponding S.cerevisiae systems, the literature for the two systems, and bioinformatic analyses using the genome sequence. Then, identify new components of each system using advanced statistical tools based on protein seq similarity and transcriptomic datasets.

Their initial ODE modelling is based on the Klipp/Hohmann model of S.cerevisiae. Their intitial modelling is of two signalling modules regulating Hog1 in S.cerevisiae. Only the Sln1 phosphorelay is included in the Klipp model, while Ssk2 is inactive in some C.glabrata isolates. Also, the Sho1 module does not signal to Hog1 in C.albicans. They also want to build ODE and stochastic models of the two organisms, and refine the models further using wet lab experiments. They want to use qualitative reasoning to describe nodes for which innformation is sparse, and predict the nature of poorly-defined links in our models with help from theoretical biologists.

Anticipated outputs are a more complete understanding of the individual stress responses in yeasts as well as of combinatorial stress responses. Further, they wish to examine the relevance of combinatorial stress responses to fungal pathogenicity and the evolution of combinatorial stress responses.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. :)

...or, all you ever wanted to know about wine yeast, but were afraid to ask
Duccio Cavalieri
Plenary Talk, Afternoon Session, 1 September (11th MGED Meeting, 1-4 September, 2008)

Volatile organics in: Grapes = 466 , Wine = 644, Difference= 178. The most ancient evidence from 3150 BC, in Egypt. However, the ecology of yeast has been mainly unknown. The probability of S.cerevisiae on a pristine grape is .0005, while per damaged grap is 25%, which means 10^4 - 10^5 (probably from clonal expansion). Proportion of damaged grapes is 1/1000. When there are many organisms (above 4%), cerevisiae is the only survivor. 88% of the S288c gene pool derives from EM93 isolated from rotten figs in Mercedes, California in 1938. Most regulatory genetic variation is due to a high rate of cis acting alleles and a small number of trans acting alleles.

Exploring the genome-environment interaction, and looking at mendelian segregation of expression profiles. One strain's colony, after 4 days' growth, produces an interesting and cohesive shape ('filigree'). Compared the parent to the first segregants. They found that 2 alleles were controlling the expression of 378 genes, or 6% of the genome. They tried to clone these genes from the recessive mutation, but failed after screening 300 false positives. Then, a few years later, tried to get the genes responsible for the phenotype via pathway analysis via a Fisher exact text followed by pathway correction. If you compare with the wt, there isn't a single significant pathway that segregates except the cell cycle stuff. Then he applied Bagel Analysis to extract absolute values and probabilities using bayesian statistics - discovered significance for amino acid transporters. They then did the same experiment, but with rich amino-acid medium, and with a minimal aa medium. The filigree strain has a mutation in SSY1, which causes a truncation. This means you lack the sensing part of the protein on the outside of the membrane, which means that the strain is blind to the amino acids in the media. This makes the yeast produce its own amino acids.

Only a small number of genes were differentially expressed based on the filigree phenotype. Ammonia mediates signalling between these cells. MEP1, MEP2, and MEP3, and PHD1 three were overexpressed in the strain. M28 is homozygous for a mutation causing non-disjunction of cells after cell division affecting AMN1 and GPA1. The daughter cell doesn't divide immediately after division, which creates 3d structures.

Means that the potential for differences in the various wine strains is quite high. The estimate of the relatedness between By4743 and Em93 (parent strain) is 90% similar. Most of the variation is not evenly distributed. Mutations in the aa, nitrogen sensing, and hap genes are highly pleiotropic. HDAC and SIR-SAS gene expression control provides the cell with a mechanism of epigenetic buffering of variation.

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. :)

..of Saccharomyces cerevisiae
Gavin Sherlock
Afternoon Session, 1 September (11th MGED Meeting, 1-4 September, 2008)

The population structure in the presence of clonal interference is markedly different from that in a classic model. They needed a system to model population dynamics in a population. They use FACS and different-colored fluorescent cells to do this. They grew the cells in a chemostat, which is seeded with equal numbers of each of the 3 color types, and then measure the proportion of the population over time. The experiment has been done 8 different times. One run, for example, shows expansions and contractions followed by one color becoming the majority. Fixation of a color is not necessarily indicative of fixation of an adaptive event (multiple adaptive clones within a population with the same color).

Using yeast tiling microarrays, they can identifiy location nucleotide differences between the evolved and parent strains. Then sequence the candidate mutations. One of the mutations they found (in cox18) called Red 266 was discovered via decreased hybridization compared to the parent. Another example was where there was a comparatively higher level of hybridization. Mutation history can help determine which strains come from which earlier parent strains with mutations of their own.

Clonal interference is important in adaptive evolution of yeast. Specifically, glucose transport and signalling through the Ras pathway were both affected. In future, they wish to directly determine which mutations are adaptive, find out how general these adaptations are, and discover the effects of the adaptive mutations, and finally - what fraction of the adaptive landscape have we explored? Will it be the same or different in the other 7 experiments?

These are just my notes and are not guaranteed to be correct. Please feel free to let me know about any errors, which are all my fault and not the fault of the speaker. :)