Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease

Abstract

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Full text

Genes 2015, 6, 24-45; doi:10.3390/genes6010024
genes
ISSN 2073-4425
www.mdpi.com/journal/genes
Article
Yeast Phenomics: An Experimental Approach for Modeling
Gene Interaction Networks that Buffer Disease
John L. Hartman IV 1,*, Chandler Stisher 1, Darryl A. Outlaw 1, Jingyu Guo 1, Najaf A. Shah 1,
Dehua Tian 1, Sean M. Santos 1, John W. Rodgers 1 and Richard A. White 2
1 Department of Genetics, University of Alabama at Birmingham, 730 Hugh Kaul Human Genetics
Building, 720 20th Street South, Birmingham, AL 35294, USA; E-Mails: cstisher@uab.edu (C.S.);
doutlaw@uab.edu (D.A.O.); guo.jingy@gmail.com (J.G.); najafali@gmail.com (N.A.S.);
dehuat@gmail.com (D.T.); ssantos@uab.edu (S.M.S.); jwrodger@uab.edu (J.W.R.)
2 Department of Statistics and Michael Smith Laboratories, University of British Columbia,
3182 Earth Sciences Building, 2207 Main Mall, Vancouver, BC V6T-1Z4, Canada;
E-Mail: rickw@stat.ubc.ca
* Author to whom correspondence should be addressed; E-Mail: jhartman@uab.edu;
Tel.: +1-205-996-4195; Fax: +1-205-975-5689.
Academic Editors: Karen E. Nelson, John Burn, Nicholas J. Schork, James R. Lupski and
Pabulo H. Rampelotto
Received: 20 August 2014 / Accepted: 12 January 2015 / Published: 6 February 2015
Abstract: The genome project increased appreciation of genetic complexity underlying
disease phenotypes: many genes contribute each phenotype and each gene contributes multiple
phenotypes. The aspiration of predicting common disease in individuals has evolved from
seeking primary loci to marginal risk assignments based on many genes. Genetic interaction,
defined as contributions to a phenotype that are dependent upon particular digenic allele
combinations, could improve prediction of phenotype from complex genotype, but it is
difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae
gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations
provides a tractable experimental approach to derive gene interaction networks, in order to
deduce by cross-species gene homology how phenotype is buffered against disease-risk
genotypes. Yeast gene interaction network analysis to date has revealed biology more
complex than previously imagined. This has motivated the development of more powerful
yeast cell array phenotyping methods to globally model the role of gene interaction networks
in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates
OPEN ACCESS

Genes 2015, 625
yeast phenomic technology, which is applied here to quantify gene X media interaction at
higher resolution and supports use of a human-like media for future applications of yeast
phenomics for modeling human disease.
Keywords: yeast phenomics; yeast models of human disease; cell proliferation phenotypes
or cell proliferation parameters (CPPs); gene interaction networks; quantitative high throughput
cell array phenotyping (Q-HTCP); genetic buffering; cystic fibrosis (CF); human-like (HL)
yeast media; ammonium toxicity; recursive expectation-maximization clustering (REMc)
1. Introduction and Perspective
1.1. Buffering of Phenotypes: Yeast Phenomic Analysis Reveals Gene Interaction Networks
Responsible for Phenotypic Variability
It is increasingly recognized that the phenotypic effects of environmental or genetic perturbation
depend upon the functional/allelic status of interacting loci [1,2]. We consider genetic buffering to
underlie phenotypic stability/variability within a population and to derive from interaction between sets
of gene variants and environmental factors, and that sets of buffering genes represent functional networks
that distinctly modulate each phenotype [3]. In S. cerevisiae, genome-wide analysis shows that genes
interact extensively [4,5]. Thus, in yeast one can experimentally define functional gene networks in terms
of their capacity to buffer, or stabilize phenotypes. Buffering networks mask functional genetic variants
subject to natural selection, and thus comprise a reservoir within populations for the complex expression
of phenotypes [3]. Gene interaction networks are constrained evolutionarily across species and diverse
phenotypes [6–9]. Therefore yeast gene interaction networks can provide insight to buffering and
variable expression of disease when yeast phenomic experiments are designed within a cellular context
analogous to human biology [10].
By yeast phenomic analysis, we mean systematic, comprehensive, quantitative analysis of
gene interaction using the comprehensive collection of yeast gene knockout/knockdown (YKO/KD)
strains [10–14]. The YKO/KD library has been widely used to identify genes that affect yeast cell
proliferation (also called fitness) on compounds of interest, i.e., gene-drug interaction [15–18]. It has
also been used for studying gene-gene interaction, where a mutation of interest is introduced to the
background of the entire YKO/KD library, and the effect of interaction between loci on fitness can be
assessed genome-wide [19,20]. The Boone laboratory is analyzing all pairwise interactions in this way,
toward a complete interaction map for a eukaryotic cell [21], and the Weissman laboratory has examined
all possible combinations for selected sets of genes, called E-MAPs (epistatic mini array profiles) [22–24].
These and other studies have demonstrated, on a genomic scale, networks of interdependent genes that
produce phenotypes. Thus gene interaction networks will be an essential component of complete models
of any cell, organism, or disease [25].
We anticipate gene interaction networks will be specific to (and can potentially define) distinct
diseases. Such disease-buffering networks will also exhibit context-specificity with respect to environmental
inputs. Most large-scale gene-gene interaction studies have been carried out pairwise in a single, or

Genes 2015, 626
limited number of media. However, genes interact with environmental factors [14,26] and in more than
pairs [27], and we are just beginning to learn about the dynamic nature of yeast gene interaction networks
with respect to more than two genetic or environmental perturbations. These observations of complexity
point to a need for greater phenotypic resolution to develop quantitative models. In this article, we
discuss our effort to develop technology to resolve yeast gene interaction networks more quantitatively
so that the YKO/KD collection can be used to model disease buffering networks more precisely.
Considering the evolutionarily conserved nature of gene interaction [28,29], prior success in using yeast
as a genetic model for human disease, and advances in technology for phenomic analysis in yeast, we
build the rationale moving forward for more extensive efforts to construct yeast phenomic models of
disease buffering networks.
1.2. The Need for Quantitative Phenotyping to Experimentally Derive Buffering Networks
To enable phenomics, we have improved methodology for quantifying yeast gene interaction on a
genomic scale. It remains technologically challenging to collect and analyze genetic interaction data due
to the combinatorial explosion inherent to such networks [30]. Most automated YKO/KD phenotyping
has been done either by microarray hybridization of total genomic DNA harvested at different time
points from liquid cultures of pooled mutants to compare relative fitness [17,31,32], or by pinning small
amounts of cell paste from mutant cultures arrayed on agar media and measuring the area of
outgrowth of the spherical culture at an endpoint [33,34]. A few studies have also performed large-scale
phenotyping by time series analysis of liquid culture arrays [14,35,36]. The quantitative high throughput
cell array phenotyping (Q-HTCP) methodology we have been developing is based on the classic method
of dilution and spotting of liquid cell suspension to agar (normally analyzed qualitatively and reported
as an image), with the modification that kinetic growth curves are obtained by serial imaging and image
analysis [16]. We discovered that growth curves generated by kinetic analysis of cell array images, if fit
to a logistic growth function, yield cell proliferation parameters (CPPs) useful for measuring gene
interaction [10,37]. In this article we illustrate improved resolution for quantifying gene interaction with
Q-HTCP data, which reveals gene X media interaction and suggests a human-like (HL) yeast media
could reduce false positive results when validating yeast phenomic results in human cells.
1.3. Are Gene Interaction Networks That Buffer Human Disease Evolutionarily Conserved?
Fundamental processes shared by eukaryotic cells such as cell cycle control and protein secretion are
genetically conserved across evolution [38,39]. The importance of such processes in disease is evident,
but whether digenic inputs involving allelic variants that interact with respect to yeast phenotypes are
predictive for expression of human disease is only beginning to be explored. A purpose of this article is
to advocate for the use of S. cerevisiae to create experimental phenomic models of gene interaction to
investigate genetic buffering of human disease.
There are multiple examples suggesting that yeast can serve as useful models of human disease. One
example is neuronal degeneration, where disease-related human proteins have been expressed in yeast
to discover yeast genes that modulate toxicity, with subsequent validation in animal models of
neurodegeneration [40–45]. Another disease model investigates the gene interaction network influencing
biogenesis of the CFTR-F508 gene product, the main cause of cystic fibrosis (CF). A yeast homolog

Genes 2015, 627
of CFTR was constructed with a mutation of the conserved disease-relevant F508 residue (Yor1-F670)
to screen the YKO/KD library for modifiers. Conservation of gene interaction was demonstrated by
comparing the Yor1-F670 phenomic screen results to the literature reporting their effects on CFTR-F508
biogenesis (when knocked down by RNA interference) [10].
In addition to modifiers of Mendelian disease, such as CF, and multifactorial diseases like
neurodegeneration, yeast phenomics holds promise for modeling organismal processes, including aging
and mitochondrial dysfunction, which are relevant to a wide variety of human disease [46–49].
Numerous other genetic models of human disease are being developed, and these span across yeast and
other model organisms [50]. A great advantage of yeast models of human disease is the relative ease of
genome-wide phenotypic analysis, nevertheless translation of these models typically necessitates a
reductionist approach, focusing on validation of a few individual genes. Thus, an important future
direction is integrative, systems level modeling of disease buffering networks.
1.4. Experimental Resources and Technology for Yeast Phenomic Analysis
To quantify pair-wise gene interaction, phenotypic measures are needed for the wild-type and mutant
cell, in the perturbed and unperturbed context [16]. The YKO/KD strain collection provides a genomic
set of mutants for systematic analyses of gene interaction. Perturbations can take the form of additional
gene mutations introduced by the synthetic genetic array method [4], small molecules, or environmental
variations. A null hypothesis, predictive of phenotype, is required so that “interaction” can be quantified
as departure from expectation [51]. The power and resolution to analyze gene interaction networks is a
function of the precision, accuracy, and quantitative resolution of phenotypic data.
To advance quantitative analysis of yeast mutant libraries, we have developed an automated workflow
with cell-array printing, time-lapse imaging, image analysis, growth-curve fitting, and quantification of
gene interaction [10,16,37,52]. Cell-array imaging can be performed manually with a commercial grade
scanner (with built-in transparency unit) or using a new imaging robot, which can be integrated with a
robotic incubator (we use the Cytomat 6001 from Thermo Fisher Scientific, Asheville, NC, USA). The
robotic Q-HTCP system has a culture capacity of 72,576 (189 × 384-cultures arrays), exceeding commercial
systems for growth-curve analysis by over 500-fold [30]. While single time point analysis of colony
outgrowth area is higher throughput for breadth of global interaction analysis [33,34], Q-HTCP is more
quantitative for greater resolution in specific disease models [10].
1.5. Examples of Yeast Phenomic Modeling of Disease in Our Laboratory
In accord with this special issue, we discuss technology, current applications and speculate that the
application of yeast phenomic modeling for human disease research is the tip of an iceberg, where the
primary challenge is to identify phenotypes for which experimental derivation of gene interaction
networks in yeast discovers gene modules relevant to variable disease expression in humans [53–58].
Cystic fibrosis is a model we are developing to investigate whether yeast phenomic analysis could
reveal a gene interaction network relevant to a Mendelian human disorder [10]. There are two unique
sources of information to validate the yeast phenomic model of CFTR-F508, one being the extensive
siRNA literature involving targets that rescue the processing defect in human cell models, and another
being genomic data from the CF GWAS consortium, which is studying large cohorts of CF patients [59].

Genes 2015, 628
Thus CF represents a promising test case for the paradigm of evolutionarily conserved yeast gene
interaction networks that buffer/modulate the expression of human disease.
In contrast to CF, a genetically tractable disease due to highly penetrant loss of function mutations at
a single locus, cancer and aging represent polygenic and complex disease. To model cancer we are studying
the genetic buffering networks of ribonucleotide reductase (RNR) and target of rapamycin (TOR), which
are evolutionarily conserved regulators of DNA replication and cell growth, respectively [16,60]. RNR
and TOR are involved in tumorigenesis and progression and comprise important targets for development
of chemotherapeutic agents [61–64]. With the hypothesis that the integration of these networks provides
a master level of cell cycle regulation (i.e., DNA and protein synthesis), an unexpected connection
between TOR and RNR suggested by this model is threonine catabolic flux, which we believe could be
regulated by TOR signaling and that we’ve found to be a mechanism for buffering depletion of dNTP
pools due to loss of RNR function [16,60]. Threonine catabolism was also found to be important for
mouse embryonic stem cell survival due to increased need for DNA replication and also a role in histone
modification [65–67]. A third model is aging, which represents a disease-associated cellular process that
can be interrogated by phenomic analysis of the YKO/KD libraries. We are using Q-HTCP to measure
chronological lifespan (CLS) by growth curve analysis of stationary phase cultures that are periodically
rescued to fresh media so that change in the viable percentage of cells can be estimated during the aging
process [68], as a function of every gene and different nutrient conditions.
In summary, every cellular process has a disease correlate and vice-versa. Gene interaction, though
rare on a percentage basis, is frequent in aggregate and contributes greatly to disease expression. Yeast
phenomics enables experimental derivation of gene interaction networks in a comprehensive and
quantitative manner that is unparalleled for modeling genotype-phenotype complexity.
1.6. Development of a Human-Like (HL) Media for Yeast Phenomic Studies
With success of yeast phenomic disease models, homologues of yeast genes will be increasingly
tested for conserved gene interaction in their human cell context. To improve the positive predictive
value of such models, we sought to reduce the potential for interaction due to uncontrolled differences
in culture media for yeast and human cells. We used Q-HTCP to analyze the YKO/KD library for gene
interaction in standard media vs. a new media we designed to more closely resemble what is used for
human cell culture. This investigation was motivated in part by work from the Botstein laboratory
showing high potassium is required in standard defined (“dropout”) yeast media to help buffer toxicity
from ammonium sulfate [69], an ingredient omitted from human tissue culture media. We also observed
ammonium toxicity, and reduced other ingredients to create a yeast media that resembles human cell
culture media but supports normal growth of the YKO/KD reference strain (BY4741). We characterized
the HL media by genome-wide Q-HTCP analysis of the YKO/KD collection to identify gene X media
interaction. Our observations of deletion strains with differential cell proliferation across media supports
the possibility that HL media could improve the translational relevance of yeast phenomic screens. We
also demonstrate in some cases that the growth inhibitory effect of small molecules depends on media.
Although beyond the scope of the paper to formally assess all of these factors in the context of an actual
human disease model, our results thus far suggest careful consideration of media type is useful for assay
optimization and interpretation.

Genes 2015, 629
2. Methods
2.1. Yeast Media and Strains
The YKO/KD library was obtained from Research Genetics (Huntsville, AL, USA) and Open
Biosystems (Huntsville, AL, USA). The genetic background for the YKO/KD library was BY4741
(S288C MATa ura3-0 his3-1 leu2-0 met17-0). Yeast media was YP (10 g/L Yeast Extract, 20 g/L
peptone) with either 2% dextrose (YPD) or 3% glycerol/3% ethanol (YPEG) as carbon source. The Cold
Spring Harbor (CSH) synthetic complete (SC) dropout media [70] was also used with either carbon
source as was the “human-like” (HL) media. The recipe for HL media was derived from the CSH SC
media with the following modifications: ammonium sulfate was removed (the normal 5 g/L was reduced
to 0.5 g/L in HL + AS media). Potassium phosphate was reduced from 1 to 0.5 gm/L. Magnesium sulfate
was reduced from 0.5 to 0.05 g/L. PABA was removed from the amino acid powder and inositol was
dropped from 0.0734 to 0.025 g/L. Leucine was reduced from 0.367 to 0.1468 g/L. The potassium
phosphate and magnesium sulfate modifications were introduced by ordering custom yeast nitrogen base
(without ammonium sulfate) from Sunrise Science (San Diego, CA, USA). The HL media recipe was
partly guided by comparison with RPMI and DMEM media components.
2.2. Quantitative High Throughput Cell Array Phenotyping (Q-HTCP)
A Caliper Sciclone 3000 liquid handling robot was used for cell array printing (384-culture arrays),
integrated with a custom imaging robotic system and a Cytomat 6001, having capacity for 189 arrays
(Thermo Fisher Scientific, Asheville, NC, USA). Images were taken every 2–3 hours and analyzed
as previously described to obtain cell proliferation parameters [10], using the logistic equation,
G(t) = K/(1 + e r(tl)), assuming G(0) < K, where G(t) is the image intensity of a spotted culture vs.
time, K is the final carrying capacity, r is the maximum specific growth rate, and l is the time that
maximal absolute growth rate occurs, when G(t) = K/2 [51].
2.3. Quantification of Gene Interaction
For the genome-wide screen, cell proliferation phenotypes (CPPs) were used to calculate gene X
media interaction in the following way: the CPP for each deletion strain was adjusted by its difference
compared to the median CPP among 384 cultures of the reference stain in YP (YPD or YPEG), and by
the difference of the median CPP of the reference strain in YP with the respective test media. After the
normalizations, the difference between the deletion strain on CSH or HL media vs. YP media was taken
as the interaction value. YP-dextrose was the control for all dextrose media and YP-ethanol/glycerol for
all ethanol-glycerol media. For the drug x gene x media interaction analysis, CPPs were obtained in the
same way and control arrays containing the same media without drug were subtracted from the respective
drug gradient arrays.
2.4. Recursive Expectation Maximization Clustering (REMc)
Clustering was performed as previously described [12], followed by hierarchical clustering and heat
map generation applying an R script (http://www.r-project.org/) to each REMc cluster. REMc was

Genes 2015, 630
performed with a 16-dataset matrix, including the ORF-deletion effect (“shift” value) on YP media and
the interaction values for defined media, for both K and L parameters. A custom java code that utilizes
Weka (www.cs.waikato.ac.nz/ml/weka) was used to generate clusters. After REMc, a python script is
used to format the clusters for analysis by the command line version of the Gene Ontology (GO) Term
Finder (GTF) downloaded from http://search.cpan.org/dist/GO-TermFinder/ [71]. GTF searches for
enrichment of Gene Ontology terms in each cluster by comparing the ratio of genes assigned to a term
within a cluster to the total number of genes tested in the experiment having that term.
3. Results
3.1. Using Growth Curve Parameters as High-Resolution, Quantitative Phenotypes
A fundamental challenge of phenomics for all organisms is quantifying phenotypes with respect to
gene interaction on a genomic scale. Every disease has multiple phenotypes with multiple different genes
contributing to each one. There is a functional spectrum among different alleles for each gene, and the
influence of a given allele on the phenotype depends on combination with particular alleles at different
loci [1]. Given this genetic complexity of phenotypic expression, yeast offers key advantages for mapping
gene interaction as comprehensively and quantitatively as possible with respect to both environment and
other genes: (1) Much of the overall fitness is encapsulated by the phenotype of cell proliferation, which
lends comprehensiveness; and (2) Cell proliferation is a continuous trait that’s straightforward to
quantify, where analysis with a logistic growth function resolves fitness into three components, providing
additional resolution for phenomic analysis and gene interaction network construction (Figure 1).
Though phenotypes are more complex in humans than yeast, it is possible to extrapolate across
species between potentially any phenotype, based upon gene interaction networks that function similarly
across evolution [6–9]. Cell proliferation is under strong selection in yeast and involves evolutionarily
conserved genes, which may be involved in the production of different phenotypes subject to natural
selection for other reasons across evolution. For example, in the yeast model of CFTR-F508, gene
interactions that influence Yor1-F670, also influence CFTR-F508 biogenesis in human cell lines [10].
In yeast, “F biogenesis” can be selected for by cell proliferation on oligomycin, whereas in humans it
is assayed by chloride transport. Although the cellular and organismal phenotypes associated with
Yor1-F670 are different from those of CFTR-F508, the network of gene interactions affecting
biogenesis of the respective proteins is similar [10]. Hardly unique to ABC transporters, quantifying cell
proliferation of yeast mutant arrays provides a powerful strategy for broadly analyzing eukaryotic gene
networks that influence a variety of human disease.
Q-HTCP is an automated method for collecting over 70,000 growth curves per experiment. Time
series image data is taken from miniature lawns of proliferating agar cell culture arrays each representing
a different defined mutant [10,16,37,52]. These data fit closely to a logistic growth function [37],
yielding independent quantitative CPPs with which to calculate gene interaction (Figure 1A).
Cell proliferation parameters represent fundamentally distinct phenotypes under differential natural
selection and thus regulated by different gene interaction networks buffering each parameter in a
context/perturbation-specific manner.

Genes 2015, 631
Figure 1. Cell proliferation parameters (CPPs) for quantifying gene interaction are obtained
from fitting Q-HTCP data with a logistic growth function. The equation and an example
growth curve are shown in panel A; CPPs are phenotypes for measuring gene interaction: K
is the carrying capacity (final growth density, quantified as average pixel intensity), L is the
time (h) it takes for a culture to reach K/2, and R is the maximum specific rate. Each
parameter is independent of the other, because G(0) is treated as unknown. The CPPs for
each graph are given in the respective table to illustrate variation in each parameter.
Of the three parameters, we have found the L parameter to be especially useful (Figure 1C).
Oligomycin growth inhibition is evident best by L in the Yor1-F508 model for CFTR-F508 [10].
Similarly, in the chronological lifespan (CLS) model of aging, where the yeast phenotype is duration of
post-mitotic survival after entry into stationary phase [72], L best reflects longevity, which is quantified
as the percentage of cells with persistent ability to regrow upon transfer to fresh media [73]. Q-HTCP
can be used for phenomic analysis of CLS by collecting growth curves at weekly intervals for the entire
genomic collection of YKO/KD strains. If other growth parameters, i.e., rate, carrying capacity and lag
remain consistent across the aging process, changes in L would estimate CLS by a rightward shift of the
growth curve over time, reflecting reduction in the number of colony forming units vs. age. Other gene-
or disease-related processes might be better modeled with a different parameter; for example,
mitochondrial function could be modeled by change in K, resulting from a relative decline in growth
after the diauxic shift (Figure 1D).
3.2. A Human-Like Yeast Media to Increase Positive Predictive Value of Yeast Phenomic Models
Cell culture media components can modulate phenotypes, representing a form of gene x environment
interaction. To increase translational relevance of yeast phenomic models to cultured human cells, we
introduced changes to defined yeast media to resemble human cell culture media. By reducing
differences between yeast and human cell media, discovery of gene interaction from yeast phenomic

Genes 2015, 632
screens can be focused on the disease network by reducing media-specific gene interaction. The
following changes were made to the Cold Spring Harbor synthetic complete media [70] to create HL
media: (1) in the yeast nitrogen base, magnesium sulfate was reduced 90% (from 0.5 to 0.05 g/L),
potassium phosphate was reduced 50% (from 1 to 0.5 g/L), and ammonium sulfate (5 g/L) was removed
(0.5 g/L ammonium sulfate was added to HL + AS media); (2) in the complete dropout powder, inositol
was reduced from 0.0734 to 0.025 g/L, leucine was reduced from 0.367 to 0.1468 g/L, and PABA
(0.0734 g/L) was removed. The YKO/KD collection reference strain, BY4741, exhibited robust cell
proliferation parameters across these media modifications (Figure 2).
Figure 2. Cell proliferation phenotypes (CPPs) for BY4741 are similar in HL and standard
yeast media. Box plots represent the central 75% (colored box), median (bold bar), and range
(whiskers). Media types include Cold Spring Harbor (CSH), human-like (HL) with or
without 0.5 gm/L ammonium sulfate (AS), or rich yeast/peptone (YP). Each media type was
prepared with either dextrose (D) or ethanol glycerol (EG) as a carbon source. CPP
abbreviations are described in Figure 1. Values of R were multiplied by one hundred. Data
are from two different 384-culture liquid arrays (teal and lavender box plots) printed onto
each media type. The main conclusion is that cell proliferation is similar for each media,
controlled for dextrose or ethanol/glycerol as carbon source. This experiment demonstrates
feasibility to modify synthetic defined yeast media to resemble that used for human cells. It
stands to reason that similarity in the media will improve reproducibility of gene interaction
across species by reducing gene interaction with media components.
3.3. Phenomic Analysis Reveals Clusters of Gene X Media Interaction
To investigate the potential significance of HL media for yeast phenomic analysis, we collected
growth curves for all (~6000) YKO/KD strains on eight different media, alternatively using dextrose or
ethanol/glycerol as carbon sources in YP (YPD/YPEG), CSH synthetic complete (CSHD/CSHEG), or
HL without (HLD/HLEG) or with 0.5 g/L ammonium sulfate (HLD + AS/HLEG + AS). Interactions were
calculated by normalizing growth of hundreds of replicate cultures of the reference strain (BY4741) on

Genes 2015, 633
each media to growth on YP media and also normalizing by the effect of the open reading frame on
growth in the YP media (i.e., YPD was used as the reference media for CSHD and HLD, while YPEG
was used for CSHEG and HLEG). We used REMc to objectively define groups of genes with shared
patterns of media interaction, and hierarchical clustering with heat maps to best visualize patterns of
interaction (Figure 3, Supplemental Data Files 1 and 2).
REMc analysis revealed six first round clusters. Cluster 1-0-0 had reduced fitness on YPEG (column 5),
which was alleviated in defined media (columns 6–8) (Figure 3C). As typical for most phenotypes, the
majority (3816) of strains exhibited little or no interaction, as illustrated by the large cluster, 1-0-5,
(Figure 3D). YKO/KD strains failing to grow on every E/G media comprised a distinct cluster (Figure
3B at bottom, extreme negative K and positive L interaction indicates no growth in columns 5–8 and
13–16), and consisted of genes enriched for GO Terms related to respiratory function as expected (see
also cluster 1-0-2 in Supplemental Data Files 1 and 2). Cluster 1-0-1 highlights gene deletions that have
reduced fitness on defined media with non-fermentable carbon source, but not rich media or media with
glucose as the carbon source; moreover, more strains display this phenotype on HL than on CSH media
(Figure 3E). Cluster 1-0-1 was enriched for genes functioning in mitochondrial and ribosomal processes,
consistent with roles in buffering combined perturbations of carbon and nitrogen availability (see
Supplemental Data Files 1 and 2).
Within cluster 1-0-0 (Figure 3C), several of the strains have reduced fitness on CSHD media. This
set of genes was better highlighted in the second round cluster 2-0.0-1 (Figure 3F). There was no
enrichment in cellular processes annotated by gene-ontology for clusters 1-0-0 or 2-0.0-1, which is often
true for interactions modulating relatively unstudied phenotypes. However, we did find genes in cluster
2-0.0-1 that are implicated in amino acid metabolism, amino acid permease trafficking and ammonium
efflux (Figures 3G), consistent with a report from the Botstein laboratory suggesting that up-regulation
of amino acid biosynthesis and excretion is necessary to buffer ammonium toxicity [69]. In support of
this model, Lst4 is involved in trafficking of Gap1, the general amino acid permease [74]. The gap1-0
strain had no phenotype, possibly due to redundancy among amino acid permeases, and thus we
hypothesize that Lst4 could regulate a module of permeases such that its functional loss (but not
disrupting any single permease), would alter fitness in the context of ammonium toxicity introduced by
the CSH media. The growth inhibitory phenotype suspected to be due to ammonium sulfate in CSH
would be alleviated by its removal from HL media [69]. Similarly, deletion of MEP1, the high flux
ammonium transporter reduced fitness in CSH media, but not HL. Deletion of VPS17 and VPS24, which
are involved with protein sorting to the endosome and vacuole also appear to buffer CSH media, based
on their being relatively dispensable in HL media (Figure 3H), perhaps implicating their function too in
the regulation of amino acid or ammonium permeases. Another gene in this cluster, ORT1, is implicated
in ammonium toxicity in humans. ORT1 is required for arginine biosynthesis due to its function as a
mitochondrial ornithine transporter, and its human ortholog ORNT1 is causative of the recessive disease,
hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (http://omim.org/). In summary, cluster
2-0.0-1 exhibits a pattern indicative of genes that buffer ammonium toxicity based on the functional
requirement for maintaining fitness in CSH but not HL media. While some genes in the cluster have
putative relationships between their known functions, others may point to novel molecular activity or
new connections between known activities [27,60] (see also Supplemental Data Files 1 and 2).

Genes 2015, 6 34
Figure 3. REMc reveals gene X media interaction modules. Q-HTCP was performed for the YKO/KD collection on the indicated media, and
interactions were calculated for defined media using YPD or YPEG as a control (see text and methods). REMc was used to mine interaction
values derived from K and L for shared patterns. (A) A color scale was used to visualize interaction values, which are positive/blue with respect

Genes 2015, 635
to the K phenotype if YKO/KD strains have higher fitness in defined than YP media, relative to the reference strain. Conversely interactions
are negative/brown with respect to L (less time required to reach K/2) if YKO/KD strains exhibit a relative increased in fitness on defined vs.
YP media; (B) The entire data matrix (root cluster) was analyzed by hierarchical clustering (before REMc). The x-axis order (media conditions
and growth parameters) is the same in all panels; (C–E) The first round of REMc yielded six clusters, three of which are shown. The YP “shift”
refers to the difference between the respective YKO/KD strain and the median of the reference (see Figure 2 and panel H); (C) Cluster 1-0-0
contains 456 YKO/KD strains, many of which exhibit lower fitness (lower K and longer L) on YPEG media (column 5 and 13) that is partially
alleviated when grown on defined media (columns 6 (CSH/EG), 7 (HL/EG) and 8 (HL + AS/EG)); (D) Cluster 1-0-5 contains a majority (3861)
of YKO/KD strains that exhibit similar phenotypes on all media; (E) Cluster 1-0-1 contains genes that share reduced fitness on non-fermentable
media (columns 6–8 and 14–16). Some strains reveal this phenotype on HL media (columns 6, 8, 14, and 16), but not YPEG or CSHEG defined
media (columns 5, 7, 13 and 15); (F) As part of the REMc workflow, clusters are iteratively analyzed until terminal clusters are obtained. Cluster
2-0.0-1, obtained in the second round from cluster 1-0-0, contained strains with low fitness in CSHD (column 3), but not YPD, HLD, or HLD
+ AS (columns 1, 2 and 4). A sub-cluster (red box) contains YKO/KD strains whose functions are listed in the table (G), with the data for
particular genes discussed in the text given in (H), where the interaction for each gene on each media type is plotted for the K parameter. Green
diamonds indicate the ORF effect, or “shift” (difference between deletion strain and reference strain on YPD or YPEG, respectively) by which
data are normalized/shifted [10].

Genes 2015, 6 36
3.4. Resolving Drug-Media Interaction by Q-HTCP across Drug-Gradients
The YKO/KD collection is frequently used to determine gene-drug interaction profiles. After finding
an abundance of gene X media interaction (Figure 3), we investigated media-dependence of drug
responses. Q-HTCP was performed on different media containing the same drug concentration gradients.
Desired concentrations of drug were added to 10 mL of media and poured into a monowell plate that
was tipped at an angle (by overlapping the bottom plate on its lid by about an inch) in order for the media
to solidify as a wedge at one end. Next, the plate was laid flat and 30 mL of media without drug was
layered over the wedge, creating a diffusion gradient. 384-cell arrays (16 × 24), consisting of 8 different
strains, with 24-cultures per row, and strains arrayed in rows 1–8 repeated in rows 9–16 (to control for
evenness in the gradient), were printed onto the drug gradient plates and control media without drug.
Q-HTCP was performed and the growth curve parameter array of the control (no drug) was subtracted
from the drug gradient plate. The change in L across the gradient was compared between different media
(Figure 4). Eight strains harboring different mutants altering drug efflux (PDR mutants) or permeability
(ERG mutants) were tested to investigate whether these factors influence the phenotype associated with
particular compounds.
Media and genetic background dependence of growth phenotypes were observed for some compounds,
including hygromycin and bortezomib (Figure 4). Hygromycin selection [75] was strong in YPD
(Figure 4A) and HL media (Figure 4B), but not on standard CSH media (Figure 4D). The addition of
0.5 mg/mL ammonium sulfate to HLD media (1/10 of what is added to CSH) slightly reduced growth
inhibition by hygromycin (Figure 4C), suggesting it reduces hygromycin efficacy. Results for hygromycin
are reminiscent of reduced efficacy of G418 with ammonium sulfate [19].
Bortezomib (Velcade, Millennium Pharmaceuticals, Cambridge, MA, USA) was approved in 2003
for myeloma treatment, and is thought to inhibit the proteasome by binding to the 26S subunit, although
its anti-cancer effect is not fully understood. Bortezomib exerted little or no growth inhibitory effect on
the reference strain (BY4741) in YPD. When a drug does not inhibit growth, it could be due to low
concentration, lack of permeability, extrusion by the efficient yeast drug efflux (pleiotropic drug
resistance, PDR) system, absence of the target, failure of the drug to sufficiently inactivate the target,
the target being unrequired under the growth conditions, and/or buffering of the physiological effect of
the drug. To assess the possibility of a drug efflux or permeability mechanism, the panel of strains,
including mutants in ergosterol biosynthesis, pleotropic drug resistance, and a chimeric fusion of the
PDR1 DNA-binding domain with the transcriptional repressor domain of CYC8 (Pdr1-Cyc8) [76], were
tested with a concentration gradient of 13 μM bortezomib in YPD, YPEG, HLD, and HLEG (Figure 4E–H).
Bortezomib was found to interact with media, drug efflux, and permeability. This result confirms a
previous report that drug efflux function influences sensitivity to bortezomib [77], and we found further
enhancement of sensitivity with HL media and ethanol/glycerol as the carbon source. Not much of a
growth phenotype was observed on YPEG (Figure 4E–H), suggesting the combination of carbon and
nitrogen sources influence drug responsiveness. The drug efflux mutants we tested were different from
those previously examined [77], and we found that a chimeric protein constructed by Stepanov et al. was
effective for sensitizing to bortezomib [76]. ERG3 deletion also influenced growth inhibition by
bortezomib on HLEG media, presumably by affecting membrane permeability.

Genes 2015, 637
Figure 4. Drug sensitivity can depend upon media type, cellular efflux and permeability of
drugs, or interaction between media and efflux or permeability. The experimental description
is given in the text. The blue color scale indicates decreased fitness (increased L), with the
range (hours) indicated in parentheses beside media type labels where relevant (panels E and F).
Numerical interaction values are printed in each cell. Gray color indicates that no growth
curve was observed (complete growth inhibition at the particular drug concentration). The
cell array data was rearranged in the heat maps to assist visualization (rows 1/9, 2/10…8/16
were grouped). (A–D) Gradient plates were poured with 500 g/mL hygromycin in (A) YPD,
(B) HLD, (C) HLD+AS, and (D) CSHD; (E–H) Gradient plates were poured with 5 μg/mL
bortezomib in (E) HLD, (F) HLEG, (G) YPD, and (H) YPEG.
In summary, gradient experiments assist optimization of drug screening conditions by surveying a
wide range of concentration in multiple different media and with multiple strains. Identification of a
media type that confers enhanced sensitivity can reduce the drug cost for a genomic screen. Likewise,
drug efflux or permeability mutations could be introduced into the YKO/KD libraries if needed [19,20],
as in the case of bortezomib. Furthermore, gradient plate Q-HTCP can be used to quantify known
gene-drug interactions (e.g., target or buffering genes) to help optimize a range of concentrations for a
phenomic screen [16]. The identification of drug X media interaction (e.g., differential drug sensitivity
of the wild type genetic background on fermentable vs. non-fermentable media) may point to comparative

Genes 2015, 638
phenomic screens to discover differential gene interaction networks informative about the impact of
biological context (e.g., Warburg effect) on drug resistance networks.
4. Discussion
Can Yeast Phenomic Models Aid Construction of Quantitative Genetic Networks that Predict Disease?
The genomic collection of S. cerevisiae yeast knockout and knockdown mutants has been a boon for
surveying genotype-phenotype complexity [21]. Prior to construction of the YKO/KD library, the
vastness of gene interaction space was unappreciated. Q-HTCP was developed as a cell array-based
growth curve technology so that the large number of mutant phenotypes can be better resolved and gene
interaction more precisely quantified, which is complementary to higher throughput, less quantitative
methods. The ability to explore gene interaction in greater detail, with increased sensitivity and
specificity comes with the constraint of more focused sets of questions and/or models, but may lead to
better understanding of complex gene networks in the context of specific disease models [10].
Most high-throughput yeast phenotyping methods measure fitness endpoints [30]. In contrast,
Q-HTCP obtains kinetic growth, likened to OD of liquid culture, but with much greater throughput, so
that fitness can be further analyzed in terms of distinct components of fitness including carrying capacity,
maximum specific rate, and time to reach half-carrying capacity [37]. For example, carrying capacity
(final growth density) of cultures on glucose is affected by the respiratory function of yeast, because
inefficient respiration results in reduced biomass accumulation following the diauxic shift (i.e., during
growth without glycolysis), but may not affect other logistic growth parameters. By contrast, YKO/KD
strains may exhibit differences in lag time if they carry mutations disrupting functions that influence efflux
of the growth inhibitory compound. This phenotype would be captured by the time to half-carrying
capacity (the L parameter) even if the carrying capacity or the maximum specific rate were unaffected [10].
Specific classes of mutants could be detected by considering the interaction pattern across all parameters:
for example cell cycle checkpoint mutants might be identified by a short L with an accelerated maximum
specific rate (due to checkpoint failure) and a reduced carrying capacity (due to cell death) [78,79]. These
are just a few examples of the overall theme, which is that genetic buffering of fitness can be further
resolved with Q-HTCP to increase the resolution of gene interaction networks, both quantitatively and
in terms of different CPPs.
Aspects of Q-HTCP development in our laboratory currently include: (1) the use of commercial liquid
handling robotics for cell array printing; (2) development of imaging methods and image analysis
software to convert images to growth curves [16]; (3) use of a logistic growth model to fit Q-HTCP data
so that CPPs can be used for quantifying gene interaction (Figure 1); (4) development of approaches to
optimize screening conditions for phenomic studies, such as media modification (Figures 2 and 3) and
gradient array analysis (Figure 4); (5) incorporation of the synthetic genetic array method to carry out
phenomic screens of gene x gene interaction [20]; and (6), development of data mining tools including
REMc [12]. Future development of the technology will focus on its application to additional disease
models and integration of yeast gene interaction networks with other omic data to more fully understand
disease expression (Figure 5).

Genes 2015, 639
Figure 5. Yeast phenomic models can serve to discover gene interaction networks underlying
expression of disease phenotypes. Disease-buffering gene interaction networks are derived
experimentally by disease-relevant perturbation of the yeast gene knockout/knockdown mutant
library followed by measurement of gene interaction with Q-HTCP. The resulting gene networks
are queried for evolutionary conservation to generate hypotheses regarding their relevance to
human phenotypes. Hypotheses can be tested in translational models, leading to model
validation or refinement, with iterative yeast phenomic analysis for further refinement.
We suspect human genotype-phenotype complexity originates substantially from cellular processes
shared in common with yeast. Thus yeast phenomic models of human disease could inform the basic
biology of gene interaction networks at the same time as variable disease expression, functioning for
both “basic” science [80] and “translational” insight intended for validation in higher eukaryotes [50,81].
The experimental convenience and eukaryotic relevance of the YKO/KD collections, together with the
development and application of Q-HTCP techniques provide a powerful opportunity to discover gene
interaction networks underlying disease biology.
5. Conclusions
Work with the YKO/KD library has revealed eukaryotic gene interaction networks to be extensive,
revealing vast phenotypic complexity even in a single-cell organism. Here we report, even when cell
proliferation of the reference strain is similar between different media, hundreds of the YKO/KD strains
have growth phenotypes, thus revealing extensive gene X media interaction. The fact that animals have
additional organismal complexity is perhaps the strongest argument for using S. cerevisiae to model
gene interaction networks [82]. Cellular functions and the genes carrying them out are conserved from
yeast to human [83]. Thus gene interactions that further modulate these processes may also be usefully
modeled across species. Many technical and biological factors will impact the implementation and
translational success of any particular yeast phenomic disease model. Cell culture media is one such
factor and the use of HL yeast media may increase positive predictive value for disease translation in
some human cell models. In any case, awareness of the potential for gene x media interaction is likely
to be beneficial. A challenge not addressed in this article, but critical to the success of yeast phenomics
for modeling disease-relevant gene interaction networks, is development of computational methods that
leverage phenomic and other omic data to extract biological insight [84]. As yeast phenomics and other
gene interaction techniques to study human disease advance, principles for genetic buffering in humans

Genes 2015, 640
will emerge and give rise to iterative models employing human data and yeast experiments [3]. An
innovative approach in this regard is the Resilience Project, which seeks to identify, from within the
non-diseased human populations, buffering loci that harbor the capacity to reduce phenotypic
manifestations of disease-associated mutations [85]. The development of a workflow to identify buffering
networks in people could leverage yeast phenomic models to help mine for gene interaction networks
underlying disease expression in human populations. Such understanding will advance disease diagnosis
and provide new targets for personalized/precision management of disease phenotypes.
Acknowledgments
The authors thank the funding agencies, which enabled the work, including Howard Hughes Medical
Institute Physician Scientist Early Career Award 57005927, American Cancer Society Research Scholar
Grant 10-066-01-TBE, National Institutes of Health R01 AG043076, and NIH P30 grants DK072482
and DK056336. The authors thank Brett McKinney for advice on computational aspects of work,
Alexander Stepanov and Mary-Ann Bjornsti for sharing the Pdr1-Cyc8 fusion strain, and Stephen Friend
and Eric Schadt for discussions about the Resilience Project.
Author Contributions
John L. Hartman conceptualized and wrote the manuscript. Chandler Stisher and Darryl A. Outlaw
performed the Q-HTCP experiments. Najaf A. Shah and John L. Hartman considered application of the
logistic model. John W. Rodgers and Richard A. White performed the Q-HTCP analysis and gene
interaction modeling. Dehua Tian, Jingyu Guo, John L. Hartman, and Sean M. Santos developed REMc
and performed analysis.
Conflicts of Interest
John L. Hartman has a financial interest in Spectrum PhenomX, LLC, which aims to commercialize
Q-HTCP technology. All other authors declare no competing interests.
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