Building mammalian signalling pathways with RNAi screens

Transcription

F O C U S O N M O D E L L I N G C E L L U L A R SRY
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Building mammalian signalling
pathways with RNAi screens
Jason Moffat and David M. Sabatini
Abstract | Technological advances in mammalian systems are providing new tools to identify
the molecular components of signalling pathways. Foremost among these tools is the ability
to knock down gene function through the use of RNA interference (RNAi). The fact that RNAi
can be scaled up for use in high-throughput techniques has motivated the creation of
genome-wide RNAi reagents. We are now at the brink of being able to harness the power of
RNAi for large-scale functional discovery in mammalian cells.
Small interfering RNA
(siRNA). A class of 19–22nucleotide-long RNA molecules
that interfere with the
expression of genes by eliciting
the RNAi response. siRNAs are
short double-stranded RNA
molecules with 2-nucleotide
overhangs on either end,
including a 5′ phosphate
group and a 3′ hydroxyl group.
They can be artificially
introduced into cells to bring
about the knockdown of a
particular gene.
Interferon response
A primitive antiviral response
to dsRNAs of >30 base pairs,
which triggers the sequencenonspecific degradation of
mRNA and the downregulation
of cellular protein synthesis.
Whitehead Institute,
9 Cambridge Center,
Cambridge,
Massachusetts 02142, USA.
Broad Institute of
Massachusetts Institute of
Technology and Harvard,
320 Charles Street,
Cambridge,
Massachusetts Institute of
Technology, Department of
Biology, 77 Massachusetts
Avenue, Cambridge,
Correspondence to D.M.S.
e-mail: sabatini@wi.mit.edu
doi:10.1038/nrm1860
Genome sequencing has ushered in the need for new
technologies for the functional annotation of human
genes. This has been particularly difficult in mammalian
systems because of the lack of tools to probe gene function systematically and quickly. RNA interference (RNAi)
offers the cell biologist an approach to perturb gene function that can be applied in a high-throughput fashion on
the cell or organism scale1,2. RNAi is a sequence-specific,
post-transcriptional, gene-silencing process3–6 that
is mediated by double-stranded RNA (dsRNA) molecules7. The effectors of RNAi are small interfering RNAs
(siRNAs) that are processed from longer precursors by a
ribonuclease known as DICER. One strand of the siRNA
functions as a template for the RNA-induced silencing
complex (RISC) to pair to, and cleave, a complementary
mRNA. Cleaved mRNAs are then rapidly degraded.
Long dsRNAs (400–700 base pairs) induce specific
and potent gene silencing when introduced into worms,
flies or plants3–6. RNAi libraries that target most genes in
worms and flies have been successfully used in screens
that have provided important insights into gene functions8–12. In mammalian cells, long dsRNA triggers a
nonspecific interferon response13; therefore, siRNAs14,
short hairpin RNAs (shRNAs)15–19, or short hairpin RNAs
in a microRNA (miRNA) context (shRNA-mirs)20–22 must
be used to prevent these nonspecific effects. In the interferon response, dsRNA molecules of >30 base pairs bind
to and activate the protein kinase PKR and 2′,5′-oligoadenylate synthetase, which go on to stall translation and
cause mRNA degradation in a sequence-independent
manner23,24. Commercial vendors and academic laboratories have now created sets of chemically synthesized
siRNA reagents and have also constructed, or are in
the process of constructing, large shRNA- or shRNAmir-based libraries in retroviral22,25,26, adenoviral27 and
lentiviral vectors28.
This review outlines several of the screens that have
set the stage for RNAi loss-of-function studies in mammalian cells and summarizes the steps that are necessary
for component discovery in signalling pathways. In addition, we suggest avenues for component classification
and systems analysis that can be used to delineate signalling networks. Finally, we use a signalling pathway that
is studied in our laboratory — the mTOR pathway — as
an example of how RNAi screening could hypothetically
derive the architecture of a pathway much faster than
traditional approaches.
The beginnings of mammalian RNAi screening
A dozen or so RNAi screens have looked at the effects
of the systematic knockdown of 50 or more genes in
mammalian cells, and a survey of some of these studies
provides a quick overview of the RNAi-based screening
approaches that have been used in mammalian systems
(BOX 1). Full genome-wide screens have not yet been
completed in mammalian cells.
siRNA screens. The transfection of human cells with
chemically synthesized siRNAs is an easy way to silence
a gene of interest. For example, this method was used
to discover modulators of apoptosis that were produced
in response to tumour-necrosis factor (TNF)-related
apoptosis-inducing ligand (TRAIL), a member of the
TNF superfamily. Aza-Blanc and colleagues reverse
transfected 510 siRNAs that targeted 510 genes (380
kinases, 100 unknowns, 30 others) into HeLa cells,
and monitored cell viability by measuring the reduction in alamarBlue staining after TRAIL induction29.
AlamarBlue is a non-toxic stain that changes from
the oxidized, indigo-blue, non-fluorescing state to the
reduced, fluorescent-pink state under the influence
of cytochromes and other reducing agents that are
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Short hairpin RNA
(shRNA). A short RNA that
contains sense and antisense
sequences from a target gene
that are connected by a hairpin
loop. shRNAs can be
expressed from a pol-III-type
promoter or in the context of a
microRNA by pol II promoters.
Following processing of the
shRNAs, the resulting siRNAs
can decrease the expression of
a gene that has
complementary sequences by
RNAi.
produced during periods of increased proliferation.
This screen identified a number of genes for which
knockdown either desensitized or sensitized cells to
TRAIL-induced apoptosis. For example, knockdown
of DOB1, a gene that is required for progression of the
apoptotic signal through the intrinsic mitochondrial
cell-death pathway, desensitized cells to TRAILinduced apoptosis. This was the first large-scale
demonstration that chemically synthesized siRNAs
could be used in a functional screen. More recently,
screens that targeted human kinases and phosphatases
in HeLa cells have further demonstrated the utility
of chemically synthesized siRNAs for mammalian
loss-of-function genetics30,31.
Box 1 | Approaches to RNAi-mediated gene knockdown in mammalian cells
Three types of small RNAs can be used to silence gene function by RNA interference
(RNAi) in mammalian cells. Small interfering RNAs (siRNAs) are short double-stranded
RNAs (dsRNAs) that are typically 19–22 bp in length with 2-nucleotide overhangs on
either end, including a 5′ phosphate group and a 3′ hydroxyl group. siRNAs can be made
by chemical synthesis, in vitro transcription, RNAse III digestion of dsRNAs or by PCR
expression cassettes. They can then be introduced into target cells by transfection to
cause transient silencing of a target gene (see figure, part a).
Alternatively, short hairpin RNAs (shRNAs; see figure, part b) or shRNAs in a microRNA
context (shRNA-mirs; see figure, part c) are constructed in a plasmid backbone and can
be transfected or packaged into a virus and transduced into target cells. Transduction
results in the stable integration and expression of an shRNA or shRNA-mir in the target
cell. shRNAs are expressed from an RNA polymerase III (RNA pol III) promoter and
shRNA-mirs from an RNA pol III or an RNA pol II promoter44. shRNAs are produced as
single-stranded molecules of 50–70 nucleotides in length and form a stem–loop
structure in vivo. A 5–10-nucleotide loop keeps the complementary 19–21-nucleotide
stem sequences in close proximity to allow base pairing to occur. shRNAs exit the
nucleus and are recognized and cleaved at the loop by the nuclease DICER and enter
the RNA-induced silencing complex (RISC) as siRNAs. shRNA-mirs behave like miRNAs
and are transcribed into a single-stranded RNA molecule, and the complementary
sequences base pair to form a dsRNA hairpin molecule that is referred to as the primary
polyadenylated miRNA structure (pri-miRNA). DROSHA, a nuclear enzyme, cleaves the
base of the hairpin to form the miRNA precursor pre-miRNA of ~70–90 nucleotides with
a 2-nucleotide 3′ overhang36. This pre-miRNA molecule is actively transported out of the
nucleus into the cytoplasm by exportin-5, a carrier protein41,42. The DICER enzyme then
cuts 20–25 nucleotides from the base of the hairpin to release the mature miRNA79.
The artificial target sequence in the shRNA-mir is incorporated into the RISC as an
siRNA to cause target knockdown.
a
siRNA
5′
3′
19–22-mers
3′
5′
Transfect
Transient effect
of perturbagen
Target cell
b
shRNA
Transfect
5′
3′
c
Plasmid
shRNA-mir
5′
Stable integration
of perturbagen
Virus
Package
into virus
Target cell
3′
DROSHA
DICER
Targeting sequences are
cloned in the context of
a microRNA, such as mir-30
Several pitfalls are associated with chemically synthesized siRNAs, however. First, siRNA molecules only
cause transient inhibition of gene expression, as they
are unstable and become diluted when cells multiply.
A large excess of siRNAs must be used against targets
to offset these drawbacks and in some cases, multiple
doses of siRNAs must be given to achieve knockdown.
RNAi-knockdown efficacy with transfected siRNAs is
therefore low towards targets with high-turnover transcripts or persistent proteins. Second, many cell types (for
example, primary cells) are difficult to transfect with high
efficiency. One group of researchers is striving to overcome this problem by combining reverse transfection with
electroporation methods32. Third, chemically synthesized siRNAs are expensive. Because of the relative ease
of siRNA screening, industry and large academic consortiums such as Mitocheck are exploiting this technology
for drug-target validation and comprehensive analyses.
Plasmid shRNA screens. Not long after mammalian
RNAi was described, several groups discovered that plasmids that expressed shRNAs from an RNA polymerase III
promoter could also be used to efficiently silence gene
function16,18. The first application of this technology in
a screen examined how knockdown of de-ubiquitylating
enzyme (DUB) expression affected nuclear factor
(NF)-κB-reporter activity33. NFKB1 encodes a component of the NF-κB transcription factor that is involved
in inflammation, immune responses and protection
against apoptosis. Fifty DUBs were screened and loss of
expression of one of these — CYLD, the familial cylindromatosis tumour-suppressor gene — was shown to
enhance NF-κB activity. Additional experiments demonstrated that CYLD affects NF-κB activity through its
ability to modulate the inhibitor of NF-κB (IκB) kinase
complex through its DUB activity, and that inhibition of
CYLD increases resistance to apoptosis that is mediated
by TNFα.
A large set of approximately 28,000 sequence-verified
retroviral-based plasmid hairpins that targeted 9,610
human genes and 5,563 mouse genes was subsequently
created26. To test the performance of this large library in
a biological context, ~7,000 plasmid-based shRNAs were
individually co-transfected into HEK293T cells in multiwell plates with a green fluorescent protein (ZsGreen)
reporter that carried the PEST domain of the mouse
ornithine decarboxylase enzyme (ZsGreen–MODC
degron fusion) and a DsRed expression plasmid. The
ZsGreen–MODC reporter is normally degraded by the
proteasome and the DsRed fluorescent protein served
as a control for transfection. So, an increased green/red
signal in a single well represented compromised proteasome function due to a specific plasmid shRNA. This
screen identified a number of potential targets that
increased the accumulation of ZsGreen–MODC, which
demonstrated that large-scale, well-based transfection
screens are possible26.
Plasmid shRNA-mir screen. Because knowledge about the
biochemistry of RNAi has rapidly expanded over the past
year, another type of shRNA library has emerged22. In this
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MicroRNA
(miRNA). A small non-coding
RNA of 19–25 nucleotides in
length that regulates the
expression of genes at the
stage of protein synthesis.
Reverse transfection
A process whereby cells are
transfected with features (for
example, DNA or RNA) that are
immobilized on glass slides or
in multi-well plates.
RNA polymerase III
promoter
A promoter that uses RNA
pol III to drive the production
of 5S RNA, tRNA and other
small RNAs. U6 and H1 pol III
promoters have all the
elements that are required for
the initiation of transcription
upstream of a defined start site
and the termination of
transcription at four or more Ts.
Primary polyadenylated
RNAs
(pri-miRNA). A long primary
polyadenylated miRNA that
is transcribed by RNA pol II.
The miRNA sequence and its
reverse complement base pair
to form a dsRNA hairpin loop,
which forms the primary RNA
structure.
Microprocessor complex
A small protein complex
consisting of Drosha and
DGCR8 that is necessary and
sufficient for mediating the
genesis of miRNAs from the
primary miRNA transcript.
Pre-miRNA
A miRNA precursor that is
converted from the pri-miRNA
in the nucleus by the
Microprocessor complex and
exported to the cytoplasm by a
mechanism that is mediated by
exportin-5. The DICER enzyme
then cuts 20–25 nucleotides
from the base of the hairpin to
release the mature miRNA.
High-content image-based
screen
(HCS). A method that uses
high-resolution images as the
readout for a screen. This type
of screening is typically carried
out using automated
microscopy to acquire images.
The images are analysed by
eye or by automated image
analysis, which is sometimes
referred to as HCA (highcontent analysis).
type of library, shRNA constructs are embedded in the
context of an endogenous miRNA-precursor sequence.
Several studies have shown that, in animals, miRNAs
are transcribed by RNA polymerase II to generate long
primary polyadenylated RNAs (pri-miRNAs)34,35. The primiRNA is recognized and cleaved at a specific site by
the nuclear Microprocessor complex to produce a hairpin
miRNA precursor (pre-miRNA) of ~70–90 nucleotides36–40.
The pre-miRNA is transported from the nucleus to the
cytoplasm, where it is recognized by DICER and cleaved
to produce a mature miRNA41,42. Artificial shRNAs that
are inserted in the endogenous mir-30 sequence are
excised from transcripts and inhibit the expression of
mRNAs that contain a complementary target site21.
The assay that was described above was also used to
look for genes that are involved in proteasome function.
Co-transfection of ZsGreen–MODC, DsRed and library
plasmids allowed the knockdown efficiency of a small
set of shRNAs and shRNA-mirs with the same targeting
sequence — which was directed against a number of proteasome subunits — to be compared22. It was found that
the shRNA-mirs performed substantially better than the
shRNAs, with up to a 12-fold improvement in knockdown in the context of the same vector backbone22. This
difference was attributed to the fact that shRNAs might
be processed more efficiently into siRNAs in the context
of mir-30 (REFS 43,44).
Virus shRNA screens. An alternative approach to plasmid
transfection is to transduce cells with a virus that integrates a stable shRNA-expressing cassette into the genome
of the target cell16,45. Several groups have created, or are
in the process of creating, large-scale shRNA libraries in
retroviral-based22,25,26, adenoviral-based27 and lentiviralbased vectors28. These vectors can be used with packaging
systems to generate viruses that will integrate shRNAexpressing sequences along with a selectable marker in
various cell types including primary and non-dividing
cells.
The first library to be used in an infection-based format was from the Bernards laboratory, where 83 pools
of retroviruses from 23,742 distinct shRNAs targeting
7,914 different human genes were made and used to
infect genetically modified fibroblast cells to identify
genetic suppressors of a p53- and temperature-dependent
cell-cycle-arrest phenotype25. A total of six genes were
isolated that suppressed the growth phenotype, including p53 itself. One complication of this screen was that
most of the isolated colonies contained multiple shRNA
inserts per colony. Only those inserts that were present in
multiple independently derived colonies were analysed
in follow-up work. A way around this would be to prepare
transfection-quality DNA and virus from each shRNA
clone and screen the viruses in separate wells.
This approach has been more difficult to implement
because it requires the preparation of uniformly hightitre virus in multi-well plates where each well contains
virus that will integrate a unique shRNA-expressing
cassette into target cells. Recently, a consortium of laboratories has reported the creation of a lentiviral-based
shRNA library, as well as producing protocols for its
reproduction and application to array-based infection screening28. The library currently contains 90,000
constructs that target 11,000 human and 8,000 mouse
genes (that is, 5 shRNAs per gene). The goal for this consortium is to target most human and mouse genes. In a
proof-of-concept study, ~6,000 unique lentiviruses that
express distinct shRNAs that target 1,028 human genes
were made and used from this library in a high-content
image-based screen (HCS) to discover genes that affect
mitotic proliferation28. This was the first application of
virus to a large-scale arrayed RNAi screen.
Alternative RNAi screens. In addition to conventional
siRNA-transfection-based screens where individual
siRNAs are synthesized systematically and transfected
into cells separately or in pools, libraries that represent
enzymatically prepared siRNAs (esiRNAs) or complex
mixtures of plasmid-based shRNAs have been developed and, in some cases, validated in screens46–49. For
example, Kittler and colleagues generated siRNAs
by endoribonuclease cleavage. Briefly, cDNAs were
amplified by PCR with primers that contained the
T7 promoter sequence, and were then transcribed
in vitro with T7 polymerase to produce long dsRNAs.
These long dsRNAs were then digested with recombinant RNase III to produce siRNAs, which were
subsequently purified using an affinity column. The
advantage of this approach is that siRNAs against the
entire coding sequence of the target gene are used for
its knockdown. The disadvantage is that this siRNA
sequence diversity might cause the knockdown of a
number of unwanted targets. A library of esiRNAs
that targeted 5,305 genes was created in this manner to
examine genes that are required for cell division in HeLa
cells. This was accomplished by assaying for cell proliferation using the WST-1 substrate, which gets reduced
and changes colour in actively growing cells. Candidates
from this proliferation assay were examined further by
a secondary, high-content, video-microscopy assay, and
37 genes were identified that affect cell division49.
Another system that uses a plasmid with convergent
RNA polymerase III promoters expressing ‘siRNA cassettes’ was employed to screen >8,000 genes for factors
that affect NF-κB signalling50. In this scenario, two small
complementary RNAs are generated and must base pair
to form an siRNA that will get recognized and incorporated into the RISC. These approaches could represent
viable alternatives to costly siRNA reagents for interrogating the function of a large set of genes and, in some
cases, might reduce nonspecific effects.
Undertaking a mammalian RNAi screen
An outline for building signalling pathways using RNAi
screening is shown in FIG. 1. The first step, component
discovery, involves developing and performing the
biological screen of interest. In a poorly understood
system, a small set of targets (for example, kinases) can
be examined to look for genes in a specific functional
class or pathway that might affect the system of interest.
For more developed pathways, a genome-wide screen
can be performed to gain a comprehensive view of the
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1 Component/target discovery
• Identify genes to be perturbed
• Generate library of perturbing reagents
• Assay development and testing
• Screen
• Hit identification
2 Component classification
• Hit validation
• Bioinformatics
3 Systems analysis
• Hypothesis generation
• Epistasis experiments
Figure 1 | Identification of components in a biological
process using high-throughput RNA interference.
The steps required to build signalling pathways are as
follows. First, component discovery involves the
identification of the target genes, choosing a library of
gene-targeting reagents, developing the biological assay
of interest to meet the demands of the library, performing
the screen and identifying the hits. Second, component
classification entails separating hits that can be validated
for ‘on-target’ knockdown and gathering information
about hits that can be used as clues to facilitate the next
step. Third, systems analyses involve generating testable
hypotheses and performing epistasis experiments to order
components within a signalling network.
Epistasis
The masking of a phenotype
that is caused by a mutation in
one gene, by a mutation in
another gene. Epistasis
analysis can therefore be used
to dissect the order in which
genes function in a genetic
pathway.
Perturbagen
A reagent (for example, a
chemical or siRNA) or
condition that disrupts or
modifies the function of a
specific gene or signalling
pathway.
system. Library selection will depend on the target cell
type and whether long-term or transient inactivation of
gene products is required.
The second step is to confirm that the hits are ‘on
target’ by an examination of target transcript and protein
levels. Obtaining multiple distinct targeting constructs
per gene provides evidence that the hits are on target.
Components are then classified into organized subgroups by probing the literature and doing comparative
analyses with homologous genes in other organisms.
In the final step, which is contextual or systems
analysis, hypothesis generation takes over and additional
experiments help define gene relationships and provide
mechanistic insight. For example, systematic genetic
epistasis experiments can help to define the order of
components that function in a signalling pathway.
To summarize, the key practical issues when performing an RNAi screen in mammalian cells are assay
development, library selection, on-target validation and
performing follow-up experiments.
Component discovery
The first step to discovering components in a biological process is to develop an appropriate assay to satisfy
the aim (BOX 2). Fortunately, technology is advancing to
accommodate more sophisticated assays, such as HCS.
As a result, the spectrum of potential measurements in
a given assay is growing (BOX 2).
Next, one needs to select targets and obtain a library of
gene-perturbing reagents (BOX 1). The ideal resource for
mammalian loss-of-function genetics would consist of a
comprehensive library that is broadly available with many
of the following properties. First, the library would contain effective suppressors of all genes. Second, the library
would work in most cell types, including non-dividing
cells and primary cells. Third, it would readily allow both
pooled and arrayed screens. That is, perturbagens could be
examined one at a time or they could be mixed together
and used in groups. Fourth, the library reagents would be
validated at the transcript or protein level, or both. Lastly,
library reagents would have minimal off-target effects.
The selection of gene-perturbing reagents is dependent on a number of factors that are related to the biological process, including target cell type, cost and library
performance. At present, there are a number of resources
to choose from (TABLE 1). For transient knockdown of
genes in easily transfectable cell lines (for example, 293T
or HeLa cells), siRNA libraries are a good approach. For
long-term gene knockdown, or to knock down gene
function in difficult-to-transfect cell lines (for example, primary cells), viral-based shRNAs are the best
approach. For further details on how to choose effective
RNAi reagents, see the practical points that are raised
in REFS 51,52. The three basic formats that researchers
have adopted for high-throughput mammalian RNAi
screening are described below.
Well-based arrayed screening. In this format, each
well of a multi-well plate contains a different genetargeting reagent (for example, siRNA, plasmid shRNA
or virus). In some cases, it is desirable to group all targeting reagents against a given gene in the same well. The
advantage of this is that fewer wells are needed to screen
a given set of genes, which saves on costs. The disadvantages of this strategy are that highly potent sequences
become diluted, and that there is an increased possibility
of undesirable off-target effects (see below). Companies
that sell siRNAs are now recommending that three or
more different sequences be tested against a given target.
These targeting reagents can be introduced into cells in
multi-well-plate format by transfection or infection
(FIG. 2A).
The two main advantages of this format are that
quantitative assays can be performed in each well on
a population of cells, which easily allows for negativeselection screens, and that the constituents of each well
are known, thereby simplifying target identification.
For example, one group recently used chemically synthesized siRNA libraries that targeted all human kinases
and phosphatases to identify gene knockdowns that
affect apoptosis and that sensitize HeLa cells to chemotherapeutics, including taxol, cisplatin and etopiside30.
This study identified a long list of pro- and anti-survival
kinases and phosphatases as well a group of genes that
sensitize cells to drug treatments.
Pooled screening. Perturbagens can be screened in pools
if each is marked by a unique sequence that serves as a
molecular barcode. In the case of mammalian RNAi,
target-specific sequences that have been integrated by
a virus into a cell represent unique shRNAs that can
function as barcodes1. These sequences can be recovered by PCR using vector-derived primers that flank
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Cell microarray
A method for studying cells
that take up perturbagens and
that have been printed in an
arrayed format on the surface
of glass slides.
the hairpin-encoding DNA sequence. Fluorescent
labelling of the PCR product allows it to be identified
by hybridization to microarrays that contain the genespecific knockdown oligonucleotides.
Such molecular barcodes were first used for the
genome-wide set of Saccharomyces cerevisiae knockout
strains, where each known or predicted open reading
frame was replaced by a kanamycin-resistance marker
Box 2 | Reporter assays and high-content image-based screening
Signalling pathways can be investigated by a good reporter assay (see figure, part a).
Examples of reporter assays include: luciferase-based transcriptional reporters; proteinmodification reporters; protein-interaction reporter assays, such as the yeast twohybrid or fluorescence resonance energy transfer (FRET, a technique that measures
interactions between two tagged proteins in vivo); protein-localization reporters; and
reporters for cell size, cellular morphology, cellular internalization, secretion and many
others. Reporter assays require a method of detection such as a fluorescence reader.
Automated image acquisition and analysis is a developing detection technology that
can be used in RNA interference (RNAi) screening by opening up a world of phenotypes
that can be probed and quantified following parallel gene knockdown. When images are
collected at high magnification and analysed manually or automatically, this approach is
also referred to as high-content image-based screening (HCS). Obtaining thousands, or
hundreds of thousands, of images will lead to novel and interesting phenotypes.
Morphological profiling represents a new approach to explore phenotypic diversity in
mammalian biology. Part b of the figure shows examples of images that are obtained
from an HCS screen. The images shown here were derived from an arrayed lentiviralbased RNAi screen and highlight how silencing different genes by RNAi can affect the
morphology of a single cell type. In this case, HT29 cells were infected in an arrayed
lentiviral-based short hairpin RNA (shRNA) screen and processed with Hoechst (to stain
DNA blue), rhodamine-conjugated phalloidin (to stain actin red) and an antibody that
recognizes phosphorylated histone H3 (green) four days after the initial infection.
Each panel shows HT29 cells after knockdown of a different gene by lentiviral-mediated
RNAi. The images are taken at 10× magnification on a Cellomics Arrayscan. The scale
bars represent 50 µm. Peri-actin, intense peripheral actin staining.
a
Examples of types of reporter assays
OFF
Luciferase
Transcriptional
activation
Image-based screening
Wild type
Large, round cells,
intense peri-actin
Small nuclei and
cells
Neuronal-like,
extended processes
Large cells,
average N, fibroblast
Large cells,
cytoplasmic actin
ON
Luciferase
Protein
modification
+
b
Protein
interaction
Reporter OFF
Reporter ON
Protein
localization
Cell size
Morphology
Large, round cells,
peri-actin,
phospho-H3
Internalization
Secretion
Membrane blebbing
and a unique 20-nucleotide sequence that was dubbed
the ‘molecular barcode’53. The first large-scale mammalian shRNA libraries adopted this idea to demonstrate
that barcode screening is feasible25,26. These same libraries
were used more recently in pooled screens to look for
clones with a transformed anchorage-independent phenotype following infection with pools of retroviruses54,55.
Candidate hairpin sequences were obtained by PCR
cloning and also by identifying molecular barcodes54,55.
A particularly useful application of pooled screens is
to compare two cell populations that contain the same
gene perturbations, but only one of which is exposed
to an additional stress (for example, a drug or altered
environmental condition). Following exposure to the
additional stress and PCR amplification of the barcodes, the control population can be labelled with one
fluorescent dye and the treated population with another
dye before competitive hybridization to oligonucleotide
arrays. When a particular knockdown condition sensitizes cells to growth in the presence of a stress signal, then
differential hybridization will result and the gene will be
identified immediately. One drawback of this approach
is that pooled screening is technically challenging. For
example, it is difficult to obtain uniform pools of viruses
for infection-based screens, with some viruses overrepresented and others under-represented. In the pooled
screens that have been reported so far, there have been
no attempts to measure or estimate false-negative rates.
Nevertheless, pooled screening is a powerful approach
for investigating a large number of perturbations and
might, with optimized systems, be the fastest way to
identify a set of molecular targets in the future.
Cell microarrays. In the past two years, several groups
have provided proof that mammalian RNAi can be
adapted for use in cell microarrays56. The cell microarray
is a glass microscrope slide that is covered in cells that
have taken up reagents (cDNAs, chemical compounds,
siRNAs, and so on) from arrayed spots. It is a form of
array-based screening that has been miniaturized onto
glass microscope slides. The four approaches to mammalian RNAi that are discussed above are compatible
with the cell microarray format (FIG. 2B). siRNAs, plasmid
shRNAs, esiRNAs or virus shRNAs57 can be printed onto
microscope slides, and these printed microarrays can be
stored or used directly. Cells are cultured on glass slides
and land on printed features to create a living array of cell
clusters within a monolayer of non-affected cells. As this
technology matures, RNAi cell microarrays will provide
an economical way to systematically screen the genome.
Component validation and classification
Validation. The selection of hits from an RNAi screen
has been mostly subjective and has usually involved
ranking the hits and choosing a certain percentage of the
top and bottom ranks, choosing hits that lie 2–3 standard
deviations from the mean or median, or choosing everything above or below a certain fold-change31,58. Another
approach has been to generate a list of ‘expected’ hits
from the phenotypic assay and use this list to define cutoffs to limit the number of false positives that need to be
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Table 1 | Publicly available large-scale mammalian RNAi collections
Collection*
Genome coverage
URL
Refs
Hannon–Elledge Whole Genome
Retroviral shRNA-mir library
~85,000 constructs
http://www.openbiosystems.com and
http://codex.cshl.edu/scripts/newmain.pl
22
The RNAi Consortium (TRC)
Lentiviral shRNA library and
MISSION shRNA Mouse library
~40,000 constructs targeting ~8,000 genes
http://www.openbiosystems.com,
http://www.sigmaaldrich.com and
http://www.broad.mit.edu/rnai_platform/
28
Mus musculus
siGENOME and siARRAY libraries
Various gene families
http://www.dharmacon.com
Silencer siRNA libraries
http://www.ambion.com
Qiagen GenomeWide siRNA
http://www1.qiagen.com
GeneNet Lentiviral Human
siRNA library
150,000 siRNAs targeting 39,000 mouse mRNA transcripts http://www.systembio.com
Homo sapiens
Hannon–Elledge Whole Genome
Retroviral shRNA-mir library
~90,000 constructs
http://www.openbiosystems.com and
http://codex.cshl.edu/scripts/newmain.pl
22
Netherlands Cancer Institute
Retroviral RNAi library
http://www.biomedicalgenetics.nl/
Members/Bernards/bernards.html
25
The RNAi Consortium (TRC)
Lentiviral shRNA library and
MISSION shRNA Human library
http://www.openbiosystems.com,
http://www.sigmaaldrich.com and
http://www.broad.mit.edu/rnai_platform/
28
Silencer siRNA libraries
Human genome in pre-defined sets (for example,
druggable genome)
http://www.ambion.com
siGENOME and siARRAY libraries
~22,000 genes targeted with smartPOOL technology
http://www.dharmacon.com
GeneNet Lentiviral Human
siRNA library
200,000 siRNAs targeting 47,400 human mRNA
transcripts
http://www.systembio.com
Qiagen GenomeWide siRNA
http://www1.qiagen.com
Adenovirus based library
Unknown
http://www.galapagos.be
27
*Some defined gene sets for Rattus norvegicus are also available through Dharmacon, Ambion and Qiagen. RNAi, RNA interference; shRNA, short hairpin RNA;
siRNA, short interfering RNA.
followed up. Because genome-scale screens are equivalent
to doing thousands of individual experiments, factors that
affect the distribution of results depend on the assay and
the technology used. It might be useful to use statistical
measures of data ‘quality’ such as the Z-factor to interpret
screening results59. The Z-factor is a measurement that
takes into account the dynamic range of the assay as well
as data variability that is measured on the basis of internal
positive and negative controls to produce a ‘quality’ score.
The issue of false positives and false negatives in mammalian RNAi screening has not been broached, mainly
because there is not enough information about how each
siRNA sequence from the gene-perturbing resources
affects its target, or other potential targets. As multiple
siRNA sequences for each gene become ‘validated’, applying statistical parameters to the results of a screen will
become much more meaningful.
The approach that is adopted by most is to use
subjective criteria to generate a smaller list that can be
further validated by secondary screens. Hit validation is
crucial to increase confidence in the target genes before
classifying components into subgroups. One complication in using siRNAs in genetic screens is that the
target sequence is only 19–29-bp long, so there might
be significant sequence overlap with other transcripts.
Even though the extent of ‘off-target’ effects can be
minimized by carefully selecting target sequences, it
highlights the need to validate an identified phenotype
caused by an siRNA with a second independent siRNA
that is directed against the same transcript. Having two
or more sequences that knock down target-protein
levels and elicit the same phenotype is usually good
proof of target specificity. This can be achieved if effective antibodies exist against a given protein or by using
tagged versions of the target protein. Showing that
target-transcript levels are knocked down by northern
analyses or quantitative real-time PCR with multiple
unique sequences, and that these distinct sequences
elicit the same phenotype, is usually acceptable proof
that the hits are on target.
Off-target effects and induction of the interferon
response can also be examined to rule out nonspecific
effects of the introduced sequence by, first, examining
similar target sequences using bioinformatics; second,
examination of interferon markers such as OAS1 (REF. 60);
and third, transcriptional profiling with multiple siRNAs
that are directed against the same target61. Comparison
of the target sequence against all known transcripts, or
against the genome sequence, using alignment tools (for
example, TargetScan) will provide clues to the nature of
the nonspecific targets. In rare cases, specific shRNAs
have been reported to induce an interferon response,
MS
ES
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A Multi-well-plate-based RNAi
B RNAi-based cell microarrays
a
a
siRNAs
Plasmid
shRNAs
esiRNAs
Virus
shRNAs
Library of bacterial
glycerol stocks
or siRNAs
b
Preparation of
shRNA-expressing
virus
c
Re-array virus
into 384-well plates
for high-throughput
screening
Library mixed with
printing buffer
Preparation of
transfection-quality
DNA or siRNAs
b
Re-array siRNAs or
plasmid shRNAs into
384-well plates for
high-throughput
screening
c
Library is arrayed
onto glass slide with
microarraying robot
siRNA/esiRNA
microarray
(reverse
transfection)
Plasmid shRNA
microarray
(reverse
transfection)
Viral shRNA
microarray
(reverse
infection)
Printed microarrays
can be stored or
used directly
d
Microarrays can be
incubated with cells
and then imaged live,
or fixed and stained,
imaged and analysed
e
d
Transfection (or reverse
transfection) of siRNAs
or plasmid shRNAs
into target cell lines
Infect shRNAexpressing virus
into target cell lines
f
Assay phenotype
of interest
g
Figure 2 | Formats for high-throughput mammalian RNAi screens. A | Well-based RNA interference (RNAi) screening.
Aa | Libraries of gene-targeting reagents (bacterial glycerol stocks or chemically synthesized small interfering RNAs
(siRNAs)) are kept in multi-well plates. Ab | The libraries of gene-targeting reagents are converted into transfection-quality
DNA (plasmid-based short hairpin RNAs (shRNAs)) or siRNAs. A strategy that is commonly used is to pool multiple siRNAs
that target the same gene and array these gene-specific pools into multi-well plates. Ac | Transfection-quality DNA from
viral plasmid-based libraries can be used to make viruses in multi-well-plate format that, in turn, can be used for infectionbased screening. Viruses (Ad) or nucleic acids (Ae) are then re-arrayed into 384-well plates for high-throughput screening.
Af | Infection, transfection or reverse transfection of the appropriate gene-targeting reagents into target cells results in
gene-specific knockdown. Ag | Phenotypic plate-based assays can be performed, and wells where the target cells show a
dramatic response to the perturbation can be identified simply by their plate position (see red cells). B | Various mammalian
RNAi approaches that are compatible with cell microarrays. Ba | Libraries can be of several formats including synthesized
siRNAs, plasmid-based shRNAs, enzymatically derived siRNAs (esiRNAs) or virus-based shRNAs57. Bb | Library constituents
can be printed onto glass microscope slides at high densities. Bc | RNAi microarrays can be stored for long periods of time or
cells can be cultured on top of these arrays and then processed in an image-based assay. Bd | The cells on top of ‘spots’ that
represent specific gene knockdowns are examined automatically by analysis software56,80.
the molecular mechanism of which remains unclear60.
Furthermore, transcriptional microarray profiling can
identify large sets of genes that are not co-regulated in
response to knockdown with different RNAi reagents
that target the same gene.
As final proof of an on-target hit, complementation
tests can be carried out by: first, making mismatches
in the hairpin sequence to verify the sequence-specific
effect; second, introducing silent mutations into cDNA
clones that will not be recognized by the hairpin
sequences and then examining levels of knockdown;
and third, using hairpins that are directed against the
3′ untranslated region (3′ UTR) of the target gene
that therefore do not cause knockdown of introduced
target cDNAs. In many cases, the reintroduction of
an unwanted target might not be useful owing to the
additional effects of ectopic or overexpression effects,
so the first approach is probably the best. Once the hit
list is shortened, secondary screens or assays can be
carried out to classify components further.
REVIEWS
a RNAi robust loss of function
b RNAi partial loss of function
A
X RNAi
A
A
X RNAi A
X RNAi
X
B RNAi B
B
B
A
A
X RNAi A
B
B
A
X RNAi A
A
B
B
A
X RNAi
X RNAi
B
X RNAi B
Enhancer
X RNAi
A
X RNAi B
X RNAi
B
A
X RNAi A
A
B
B
X RNAi
A
X RNAi B
X RNAi
B
c Genetic redundancy
Y
A
B
A
X RNAi Y
B
A
X
Y RNAi X
A RNAi Y
X RNAi
B
B
A
Y
B
X RNAi
Figure 3 | Probing binary genetic interactions with RNA interference. a | Phenotypic
outcomes caused by RNA interference (RNAi) due to the complete loss of function of
gene A, gene B or both. If the function of gene A activates the function of gene B to induce
a phenotypic change, then the loss of gene A, gene B or both would block this change
(a, top panel). If the function of gene A is to inhibit gene B, then blocking the function of
gene A would alleviate gene B to induce a phenotypic change, whereas blocking gene B
or both would not change the original phenotype (a, bottom panel). b | Phenotypic
outcomes caused by RNAi due to the partial loss of function of gene A, gene B or both.
If the function of gene A activates the function of gene B to induce a phenotypic change,
then the partial loss of function of either gene A or gene B would cause a partial change in
phenotype, whereas the partial loss of function of both gene A and gene B would enhance
the resulting change in phenotype (b, top panel). If the function of gene A is to inhibit
gene B, then partially blocking the function of gene A would lead to partially active gene B
and an incomplete change in phenotype, whereas partially blocking gene B or both will
not change the phenotype (b, bottom panel). c | Phenotypic outcome caused by genetic
redundancy. If gene A and gene Y both function redundantly to affect gene B and thereby
elicit a phenotype, then knocking down gene A or gene Y will not change the phenotype,
but knocking down gene A and gene Y at the same time will cause a change in phenotype.
Classification. Component classification involves extracting information from available resources to interpret
gene relationships. Because data sets are becoming
larger and more complex as a result of using functional
genomics tools such as RNAi, companies that build datamanagement systems and academic laboratories have
developed pathway-visualization tools that simplify the
interpretation of gene relationships by creating biological
networks that incorporate genetic- and protein-interaction
data. The networks are made up of nodes that represent
genes or proteins that are interconnected by lines known
as edges. Each node is typically colour-coded on the basis
of functional annotation, and the thickness of the edge
between two nodes usually represents the strength of the
binary interaction. One caveat with this approach is that
networks that are gleaned from screens are only as good as
the quality of the incoming information. On the one hand,
hypotheses can quickly be tested, and in some cases the
validation of genetic or protein interactions can allow for
the rapid assembly of a signalling circuit. If not, an incorrect annotation can provoke misleading hypotheses and
cause researchers to waste a great deal of time attempting
to validate an idea.
Automated tools to curate the literature such as IHOP
(Information Hyperlinked Over Proteins) also assist
navigation through the literature62–64. Another recently
developed search tool called HARVESTER caches and
crosslinks public bioinformatics databases and prediction servers to provide fast access to protein-specific
bioinformatics information65. HARVESTER currently
implements the following databases: Uniprot/SWISSprot,
Ensembl, BLAST(NCBI), SOURCE, SMART, STRING,
PSORT2, CDART, Unigene and SOSUI. Curating the
literature for important genetic relationships is essential
for component classification and selecting what to follow
up at the systems level.
Systems analysis
For most of the resources that are available at present,
the efficiency of target knockdown for the entire set of
reagents is not known. Validation of libraries at the transcript or protein level will become crucial for accelerating
the analysis of hits from a screen in the future. This point
underscores the importance of having good antibodies
against all human proteins — a resource that would be
invaluable to researchers. The problem that arises by
simply examining transcript levels is that certain gene
products require >90–95% transcript knockdown to
cause significant changes in protein levels. Having multiple phenotypic assays for a given biological process might
circumvent this issue if multiple hairpins for a given gene
elicit effects in many assays.
For example, Pelkmans and colleagues targeted all
human kinases in HeLa cells by arrayed siRNA transfection with a ‘pre-validated’ library containing two hairpins
per kinase that knocked target-transcript levels down by
70% or more31. They monitored entry of vesicular stomatitis virus (VSV), Simian virus 40 (SV40) and transferrin
trafficking and also followed apoptosis by Annexin-V
staining and relative cell numbers by counting nuclei31.
The results of these assays were compiled into phenotypic
classes and ordered into a functional pattern by carrying
out two-step cluster analysis. First, hierarchical clustering
was undertaken using the results of the VSV and SV40
assays, and ten groups of kinases with correlating phenotypes were distinguished. Second, within each group,
kinases were clustered according to all the other phenotypic classes. This approach revealed the existence of
interconnected functional groups that affect endocytosis
and has provided a framework for generating and testing
hypotheses that are related to certain forms of virus entry.
This kind of analysis provides global views that might help
to piece together underlying mechanistic events.
Analysis of binary genetic interactions has been a
powerful method for defining gene relationships and
building networks in traditional genetic model systems.
This has been nicely demonstrated using high-throughput
genetics in budding yeast where a global interaction
map of synthetic-lethal interactions has been created66.
MS
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a
c
?
TSC1, TSC2
LKB1
AMPK
S6K1
b
d
mTOR
RAPTOR
Gβ L
RHEB
AKT/PKB
ERK
RSK
P
Cell size
mTOR
RICTOR
Gβ L
PI3K
Receptors
RAS
PTEN
RasGAP
?
P
P
P
AKT/PKB
AKT/PKB
S6K1
Cell numbers
Cell size
e
Cell numbers
LKB1
Growth factors
AMPK
FKBP12-rapamycin
ERK
Receptors
RasGAP
RSK
TSC1/TSC2
RHEB
PI3K
R AS
PTEN
Nutrients
RICTOR–Gβ L–
mTOR
RAPTOR–Gβ L–
mTOR
?
P
P
S6K1
RICTOR readout AKT/PKB
RAPTOR readout
Cell size
Cell proliferation
Figure 4 | A hypothetical screen for regulators of growth and proliferation. An
example of how to build a signalling pathway with RNA interference (RNAi) by
screening for regulators of p70 ribosomal S6 kinase (S6K1) and AKT/protein kinase B
(AKT/PKB) activities using (a) phosphorylation of S6K1 as a readout of cell size and
(b) phosphorylation of AKT/PKB as a readout of cell numbers. Assaying for changes in
the activation state of S6K1 or AKT/PKB using phospho-specific antibodies following
knockdown of target genes by RNAi in an arrayed format should lead to candidate
regulators of cell size and/or cell numbers. Hypothetically, a number of hits that affect
phosphorylation of (c) S6K1 or (d) AKT/PKB in a positive (genes indicated in black) or
negative (genes indicated in red) manner would result from such a screen. Part (e) shows
the mammalian target of rapamycin (mTOR) signalling pathway as we understand it
today69 and that could, in theory, be derived by carrying out pairwise genetic tests from
the hypothetical data given in parts (c) and (d). The final ordering of the pathway
components could be further refined with physical-interaction data. For example, it
is known that mTOR exists in two distinct protein complexes, as shown in part (e):
mTOR partner (RAPTOR)–GβL–mTOR controls the phosphorylation of S6K1 and therefore
cell size (hence ‘RAPTOR readout’) and rapamycin-insensitive companion of mTOR
(RICTOR)–GβL–mTOR controls the phosphorylation of AKT/PKB and therefore cell
numbers (hence ‘RICTOR readout’). These distinct mTOR complexes are distinguished by
the influence of the drug rapamycin. In the diagram, RHEB (Ras homologue enriched in
brain) represents RHEB1 and RHEB2 and AKT represents AKT1, AKT2 and AKT3.
Reverse genetics
Genetic analysis that proceeds
from genotype to phenotype
through gene-manipulation
techniques.
Preliminary experiments that were carried out in flies
suggest that double-gene knockdown with RNAi could
serve to define genetic pathways using cultured cells67.
Fortunately, with mammalian RNAi, it is possible to perform double-gene knockdown experiments to examine
pairwise interactions, and multiple targeting constructs
per gene can be used to rule out off-target effects. The
caveat with mammalian RNAi is whether robust or
partial knockdown of the target gene is achieved. RNAi
does not remove a gene from the genome, and transcriptional silencing of a particular gene is never complete.
For certain gene products, a small amount of transcript
might suffice to confer a function. For other gene
products, high levels of transcript might be necessary
to confer a function. An estimation can be obtained by
examining transcript and protein levels of a particular
gene beforehand.
Simple genetic analysis can provide information on
whether the siRNAs in question confer a complete or partial loss-of-function phenotype, and whether the genes
function in a positive or negative manner with respect to
each other to elicit the phenotype (FIG. 3a,b). For example,
if gene A activates gene B to elicit a certain phenotype,
then siRNAs that cause a partial loss of function of gene A
will result in a partial phenotype that can be enhanced by
the partial loss of function of gene B (FIG. 3b, top panel).
This kind of simple genetic epistasis analysis helps to
define the order of events in a pathway.
A particularly useful application of binary genetics
is to determine whether or not two genes function in
a redundant manner. That is, knockdown of gene A or
gene Y alone causes no phenotype, but knockdown of both
simultaneously cause a phenotype (FIG. 3c). Identifying
redundant functions requires two hits. Uncovering genetic
redundancy comes by taking reverse-genetics approaches.
That is, starting off with a genetic perturbation of interest
and looking for an additional perturbation that elicits a
particular phenotype. The effects of insults in the presence of double-gene knockdowns can also be examined
to look for enhancers or suppressors of a particular
phenotype, as was first demonstrated systematically in
yeast68. Investigating phenotypic effects through binary
loss-of-function genetics will be invaluable for delineating
signalling events.
Hypothetical screen for cell-growth regulators
Here, we attempt to use the mTOR signalling pathway as
an example of how RNAi screening could hypothetically
reveal all the known components of this network with a
single well-defined screen. This is in stark contrast to the
research history of the mTOR pathway, as it has taken
over a decade to identify many of the core components of
this growth network69. Let us hypothesize that phosphorylation of p70 ribosomal S6 kinase (S6K1) controls cell
growth, that phosphorylation of protein kinase B (AKT/
PKB) controls cell numbers and survival, and that the
upstream components that control these modifications
are largely unknown (FIG. 4a,b).
To look for additional components of the mTOR
pathway, a high-throughput screen that measures total
phospho-S6K1 and total phospho-AKT/PKB levels by
automated fluorescence microscopy and image analysis
could be performed after systematic knockdown of
genes in an arrayed format. This would result in a list
of genes that positively and negatively affect phosphorylation of S6K1 (FIG. 4c) and AKT/PKB (FIG. 4d). Following
the steps outlined above, hits would be validated and
classified into subgroups (that is, kinases, phosphatases,
GTPases, receptors) to generate testable hypotheses.
To analyse the screen results systematically, pairwise
knockdown experiments would be carried out on all the
components to determine the order in which they function in the different pathways. Hypothetically, combining
mTOR knockdown with any other component (except
PTEN (phosphatase and tension homologue deleted on
REVIEWS
Blastocyst
The stage of the developing
embryo when the number of
cells reaches 40–150, a
central fluid-filled cavity called
the blastocoel forms and the
zona pellucida begins to
degenerate. This stage lasts
approximately until
implantation into the uterus.
Tetraploid aggregation
A method that is used to
generate embryos that are
completely derived from
embryonic stem cells. The
approach provides a quick
evaluation of phenotype in
embryonic development; for
example, one can observe a
knockout phenotype by
aggregating mouse embryos
with gene-knockout or
transgenic RNAi embryonic
stem cells.
Perivitelline space
Region between the surface
of the oocyte or more
specifically the oolemma and
the zona pellucida, an
extracellular matrix
synthesized by the oocyte. The
perivitelline space has contents
that change during
development and that appear
to have various roles before,
during and after fertilization.
chromosome 10) knockdown; see below) causes reduced
phosphorylation of S6K1 or AKT/PKB, making mTOR
epistatic to all other components that would be obtained
from the screen. Similarly, knockdown of the mTORinteracting protein GβL causes decreased phospho-S6K1
and phospho-AKT/PKB levels when combined with
any other component obtained from the screen, which
indicates that mTOR and GβL function in a complex that
affects S6K1 and AKT/PKB phosphorylation. The exception to this is the combined knockdown of mTOR and
PTEN, which causes decreased phospho-S6K1 levels but
unchanged AKT/PKB levels, and led to the conclusion
that mTOR and PTEN function antagonistically to control
phospho-AKT/PKB levels. A more detailed understanding
of this signalling pathway is revealed by pairwise knockdown experiments with mTOR partner (RICTOR) and
rapamycin-insensitive companion of mTOR (RAPTOR)
where phospho-AKT/PKB levels are always affected when
RICTOR is knocked down and phospho-S6K1 levels are
always affected when RAPTOR is knocked down.
A series of systematic binary genetic tests coupled to
physical-interaction data might quickly lead to the current model for how the mTOR signalling pathway works
(FIG. 4e). Delineating signalling pathways is obviously nontrivial and, unlike in invertebrates, cell lines with mutations
in the components of the network are generally unavailable. Double-gene-perturbation analysis might not involve
lethality and might not easily reveal epistatic relationships.
However, elucidating all or many of the components from
the beginning greatly simplifies the construction of the
signalling network. In addition, primary hits that appear
confusing after the initial screen can be used to identify
feedback loops that otherwise might have been hard to
detect. For example, although AKT/PKB appears to function upstream of mTOR from the initial screen, in fact, it
also acts downstream of mTOR owing to the existence of
two distinct mTOR-containing complexes70. Starting out
with a well-defined molecular event makes the interpretation of genetic results relatively straightforward. Screening
more generalized phenotypes and combining multiple
assays will probably identify interconnected networks that
will need further refinement.
The future of knockdown technology
The amazing potential for loss-of-function genetics using
RNAi-mediated techniques has opened up a world of
potential experiments that were inconceivable less than
a decade ago. Further development of RNAi technology
will expedite the more refined control of gene expression
as well as advancing organ and organismal studies.
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Acknowledgements
We thank K. Ottina, J. Reiling, C. Thoreen and D. Guertin for
helpful comments and critical reading of the manuscript and
all laboratory members for valuable discussions. We apologize
to authors whose primary works have not been cited due to
space constraints. The work of the authors is supported by a
Natural Sciences and Engineering Research Council of Canada
postdoctoral fellowship (J.M.) and grants from the National
Institutes of Health, Keck Foundation and Stewart Trust
(D.M.S.). The authors would also like to acknowledge members of the RNAi Consortium (http://www.broad.mit.edu/rnai_
platform/) for their continued support.
Competing interests statement
The authors declare no competing financial interests.
DATABASES
The following terms in this article are linked online to:
Entrez Gene: http://www.ncbi.nlm.nih.gov/entrez/query.
fcgi?db=gene
CYLD | DOB1 | NFKB1 | RICTOR | RAPTOR
UniProtKB: http://ca.expasy.org/sprot
AKT/PKB | DICER | mTOR | PTEN | S6K1 | TRAIL | TNFα
FURTHER INFORMATION
David M. Sabatini’s laboratory:
http://web.wi.mit.edu/sabatini
Mitocheck: http://www.mitocheck.org
IHOP: http://www.ihop-net.org/UniPub/iHOP
HARVESTER: http://harvester.embl.de
TargetScan: http://genes.mit.edu/targetscan/
Access to this interactive links box is free online.

Building mammalian signalling pathways with RNAi screens

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