Brett Habermehl
July 14, 2014
Final Paper
Quantitative High Throughput Cellular Phenotyping of tet-RNR2 Genomic
Deletion Collection in Saccharomyces cerevisiae for Recursive Expectation
Maximization Clustering and Gene Ontology Term Analysis
Abstract
This study investigates the gene interaction network that buffers deficiency
in ribonucleotide reductase (RNR). RNR is the rate-limiting enzyme for producing
deoxyribonucleotides (dNTPs), and its requirement for DNA synthesis makes it an
important target for cancer treatment. The RNR complex is evolutionarily conserved
and composed of two subunits. We expressed yeast RNR2, a catalytic component of
the small subunit, using a Tet-Off system to perturb the genomic collection of ~6000
yeast gene knockout and knockdown strains. Quantitative High Throughput Cell
Array Phenotyping (Q-HTCP) was utilized to collect growth curves and measure
gene interaction in all of the double mutants, based on their growth curve
parameters. Full genome screens were performed on both glycolytic and respiratory
media, to further distinguish gene interactions that depend on these metabolic
conditions. Recursive Expectation Maximization clustering (REMc) was used to
discover genetic modules (similar interaction profiles with RNR pertubation. RNR2
interaction data were compared with other perturbations of RNR, including
hydroxyurea (HU) and Tet-Off perturbation or RNR1 (a component of the large
subunit). The clusters were analyzed using Gene Ontology (GO) Term Finder (GTF)
to look for enrichment of biological processes among genes in each cluster.
Knowledge of the genetic network that buffers RNR deficiency will help to better
understand resistance to nucleoside chemotherapy agents that target RNR and to
discover new targets that will increase efficacy of the nucleoside class of cancer
therapeutics.
Introduction
We are interested to understand pathways that buffer deficiency in
ribonucleotide reductase (RNR). Ribonucleotide reductase is the rate-limiting
enzyme for producing deoxyribonucleotides (dNTPs), which are required for DNA
synthesis. Regulation of dNTP pools is an important homeostatic process, because
excessive pools cause higher mutation rates, while pool deficiency can decrease
fitness of the cells. In yeast, RNR is made up of two subunits, the large subunit
composed of a homodimer of the RNR1 gene product, and the small subunit
comprised of a heterodimer of RNR2 and RNR4. The RNR complex is evolutionarily
conserved and RNR2 is a catalytic component of the small subunit (Hartman 2007).
In this study, The RNR2 promoter was replaced so that expression of the RNR2 gene
was dialed down using doxycycline through tetracycline-regulated (tet-off) system
with and without perturbation with hydroxyurea (an inhibitor of RNR) (Singh
2009). The doxycycline-regulated allele was introduced into the genomic collection
of ~6000 S. cerevisiae gene knockout and knockdown strains. This library was
previously constructed and is available for the study. While prior studies in this lab
have shown the effects of the RNR1 with addition of doxycycline only, this study
focuses on the RNR2 gene. Quantitative high throughput cell array phenotyping (QHTCP) was used to collect growth curves for all strains, each containing the TetRNR2 allele and knockout or knockdown of a different gene. These data were
analyzed to quantify the effect of each gene (i.e., the interaction) on the growth
phenotype in response to RNR2 limitation (Guo 2010). The composite of gene
interactions is considered as the gene interaction network that buffers RNR2
(Hartman 2007). Interaction means that the cellular growth phenotype associated
with of any particular gene mutation also depends on the mutational status of other
genes. The main question that is being asked is what genes are part of the network
that buffers, compensates, and/or influences cell proliferation when RNR2 function
is inhibited? How can we apply understanding of this network to develop better
anti-cancer therapeutic strategies that employ nucleoside chemotherapy agents?
Methods
First, to find the optimal screening conditions, doxycycline and hydroxyurea
gradient plates were used to determine the ranges of perturbation that elicit
phenotypic response for screening gene interaction. A number of different
experiments were performed at different concentrations of doxycycline and
hydroxyurea to find these conditions. The optimal conditions were found to be 0, 1,
and 2 ug/ml of doxycycline all with 5 mM of hydroxyurea and a no drug control.
Quantitative high throughput cell array phenotyping (Q-HTCP) methods were used
to measure the strength of the gene interactions across the whole genome for the
whole genome screen. Q-HTCP collects a time series of cell array images and by
image analysis converts the images for each culture to growth curves to measure
gene interaction in all of the double mutants (i.e. assessing the influence of each
gene (gene deletion or not) on growth curve parameters in response to perturbation
(Tet-RNR2 dial down)) (Singh 2009). The logistic growth function is G(t)= K/(1 + e -
r(t-l)),
where K is the carrying capacity, L is the time it takes to reach half of the
carrying capacity, and R is the rate of growth (Hartman 2007). For each gene, we
compared the phenotype over a range of perturbation to hundreds of replicates of
the reference strain (see “gray diamonds” in Fig. 2). With the Tet-off system, adding
concentrations of doxycycline down regulates the expression of RNR2. This was
performed on both glycolytic and respiratory (ethanol and glycerol instead of
dextrose as a carbon source) media, to further distinguish gene interactions that
depend on these metabolic conditions. Recursive expectation maximization
clustering (REMc) was used to discover genetic modules similar interaction profiles
with RNR perturbation. The clusters were analyzed using Gene Ontology (GO) Term
Finder (GTF) to look for enrichment of biological processes among genes in each
cluster (Guo 2010).
Results
Fig. 1 Example cell array images. The wild type and Tet–RNR2 strains are in
alternating rows. All arrays have 5mM HU. A gradient of concentration of
doxycycline is in the far left, and the middle and right are in the presence or absence
of 2ug/mL doxycycline.
Fig. 2. Measurement of gene interaction. The optimal concentrations of
doxycycline were determined to be 0, 1, and 2 ug/mL. Hydroxyurea was added at 5
mM, which helped to see the RNR2 phenotype, but did not affect growth of the
reference strain. The full genome screen was performed at the above
concentrations. The images were analyzed. The growth curves were fit to the
logistic function to obtain the cell proliferation phenotypes (CPPs, growth curve
parameters), K, R, and L. The CPPs were used to measure gene interaction by linear
regression analysis vs. the doxycycline concentration. The image on the left shows
an example of a carrying capacity (K) growth curve and the image on the right
shows an example of half the time it took to reach the carrying capacity (L). The rate
of growth (R) is not shown as there was not significant data for it. Gray diamonds
represent range of reference strain phenotype.
Fig. 3. Recursive expectation maximization clustering (REMc) of Q-HTCPderived gene interaction. Gene interaction data from the entire screen was
clustered by REMc. REMc clusters were analyzed by GO Term Finder to find GO term
enrichment. 592 unique GO terms were assigned to 112 clusters in five rounds of
clustering. Of those, there were 227 specific GO terms (fewer than 60 genes) with a
p-value of <0.05. Hierarchical clustering was used to visualize clusters and
interaction data as heatmaps. 1-0-3 can be understood as a firtst round cluster and a
parent cluster to 2-0.3-0. As seen, 1-0-3 demonstrated aggravating interactions with
respect to RNR2 deficiency and the K parameter. 2-0.3-0 is a sub-cluster from 1-0-3
that exhibits strong interactions that also involve the L parameter.
In summary, by looking specifically looking at the 2-0.3-0 cluster, we found a
large number of aggravating interactions, where deletion or knockdown of buffering
genes exacerbates the sensitivity of the cell to deficiency in RNR2 function. Many of
these RNR2 aggravating interactions exhibited similar interactions with RNR1 dial-
down and hydroxyurea perturbation. There were also many RNR2-specific gene
interactions. Some interactions were detected by only one phenotypic parameter,
while others were detected by multiple parameters. Some interactions were further
influenced by media conditions, while others were exhibited independent of the
media variables tested.
Conclusion
The gene interaction network buffering deficiency in RNR2 function consists
of hundreds of genes. Parts of this network are detected by inhibition of RNR
function with hydroxyurea, while others are only detected by dial-down or RNR2.
Parts of the RNR2 network overlap with the RNR1-buffering network, as expected
for genes with protein products functioning together in the same complex. However,
in contrast, many genes are non-overlapping between the networks. For some genes
there is a further interplay between the metabolic state of the cell (respiratory or
glycolytic), and the importance of the gene for buffering RNR2 perturbation. Further
work is needed to determine the mechanisms explaining this complex genetic
network as it currently difficult to speculate on the biological mechanisms.
Knowledge of the genetic network that buffers RNR deficiency will help to better
understand resistance to nucleoside chemotherapy agents that target RNR and to
discover new targets that will increase efficacy of the nucleoside class of cancer
therapeutics.
References
Hartman, J. L., IV. 2007. Buffering of deoxyribonucleotide pool homeostasis by
threonine metabolism. Proc. Natl. Acad. Sci. USA 104: 11700-11705.
Guo J, Tian D, McKinney BA, and Hartman IV JL. 2010. Recursive expectationmaximization clustering: A method for identifying buffering mechanisms composed
of phenomic modules. Chaos 20:026103.
Singh I, Pass R, Togay SO, Rodgers JW, and Hartman, IV JL. 2009. Stringent MatingType-Regulated Auxotrophy Increases the Accuracy of Systematic Genetic
Interaction Screens with S. cerevisiae Mutant Arrays. Genetics 181: 289–300.