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In-Depth Information
a complementary set of genomic data measured from
a different molecular perspective before conclusions are
drawn.
strain expresses a single gene from a single-copy CEN
plasmid
[25]
; and (5) the MoBY-ORF 2.0 collection, where
each strain expresses a single gene from a multi-copy 2
m
plasmid
[48]
. Importantly, only the assays using libraries
with partial gene dosage or increased gene dosage allow the
drug target to be directly identified.
In each fitness assay the strains are grown competitively
(in a pool) in the presence and absence of a small molecule,
as detailed in
Figure 8.1
(e.g.,
[11,21,25]
). In the assays
using libraries with decreased gene dosage, the more
important a gene is for resistance to drug, the more rapidly
the abundance of a given strain, and hence the associated
barcode sequences of the strain, diminishes from the
culture. The same holds true for assays using libraries with
increased gene dose; however, rather than the abundance of
a strain diminishing in the pool, the more resistant a strain
is to drug the greater the abundance will be in the culture.
All genes can subsequently be identified and quantitatively
ranked in order of their relative requirement for resistance
or ability to confer resistance to drug.
The deletion or over-expression profiling assays
described above, when used together, provide additional
support for the MOA that each assay suggests or identifies.
Again, GO analysis serves as an important tool to translate
the profile from each assay into a general MOA for the
tested small molecule. If many fitness-based profiles
are collected in a compendium, an approach analogous
to the compendium RNA expression approach described
above, can be used to predict
m
Fitness-based Chemogenomic Profiling
Approaches to Identify the Drug Target/
Mechanism of Drug Action
Non-competitive Fitness-based Chemogenomic
Profiling Assays
Yeast-based chemogenomic platforms often take advantage
of the YKO collection. The YKO collection and its variants
have been used in
600 individual chemical screens,
measured by a simple count of the number of publications
resulting from these chemical screens. Each publication
presents the results from a genome-wide screen using
a single, or sometimes a handful of small molecules, most
often generated from plate-based assays that compare the
colony sizes, that is, measurements of strain fitness, in the
presence and absence of drug to identify the deletion strains
that are sensitive or resistant to drug (
Box 8.1
). The sensi-
tivity of each individual strain identifies a chemical
>
genetic
interaction that can be of several types and often provides
insight into the function of a gene. A deletion strain that is
sensitive to drug indicates that the deleted gene function is
required for resistance to drug. The function of the gene may,
for example, be to buffer the drug target pathway, to transport
the drug out of the cell and/or to detoxify the drug. The
function of a gene, that when deleted results in resistance to
drug, might be to import the drug into the cell, to activate the
drug, or even might identify the drug target.
The fitness measurements for each individual strain,
when combined, comprise a chemogenomic profile for the
tested small molecule. The non-competitive assays rarely
include heterozygous strains, and thus, they rarely test for
drug-induced haploinsufficiency (see HIPHOP section).
As a result, the target of the molecule is seldom identified
(e.g.,
[21,26,47]
). However, a profile often provides
support for the identification of the general MOA of the
small molecule. Gene Ontology (GO) analysis of a profile
is useful for identifying the pathways associated with
sensitivity/resistance to the tested molecule,
e
the MOA of a query
molecule.
Compendiums of non-competitive fitness-based profiles
can readily become much more powerful by adding only
a few profiles of, usually related, small molecules, as they
provide sufficient resolution to resolve slight differences
between similar MOAs (examples include
[26,49,50]
).
Mechanistic insights derived from existing chemogenomic
profiles have also been proved to directly translate into
mammalian cells and/or to be of clinical relevance in
several cases
[51
53]
. Only a few chemogenomic studies
have generated large-scale datasets to date
[21,47]
. Clearly,
the more chemogenomic profiles in a dataset, the more
powerful and valuable the dataset becomes in identifying
general MOAs of small molecules while also providing
insight into gene function
[47,54]
.
e
towards
inferring its MOA.
Competitive Fitness-Based Chemogenomic Profiling
Fitness-based chemogenomic assays that allow the pooling
of strains require that the yeast library be barcoded. Five
barcoded libraries exist: (1) the YKO homozygous and
haploid non-essential gene deletion collection
[10,11]
; (2)
the YKO heterozygous deletion collection
[10,11]
; (3) the
Decreased Abundance by mRNA Perturbation (DAmP)
[22]
collection
[23]
; (4) the Molecular Barcoded Yeast
Open Reading Frame (MoBY-ORF) collection, where each
HIPHOP
Although much of the discussion so far applies to the
chemogenomic platform described by HIPHOP (because
this platform has been explored so extensively) a section
devoted to the discussion of HIP and HOP is warranted.
The ability to pool yeast deletion strains in fitness assays
(
Figure 8.1
,
Box 8.1
) enabled the development of a che-
mogenomic platform that allows genome-wide screens to
