Building mammalian signalling pathways with RNAi screens
Transcription
Building mammalian signalling pathways with RNAi screens
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 MS ES VTI EEW 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 02139, USA. Massachusetts Institute of Technology, Department of Biology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, USA. Correspondence to D.M.S. e-mail: [email protected] 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 NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 7 | MARCH 2006 | 177 © 2006 Nature Publishing Group REVIEWS 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 178 | MARCH 2006 | VOLUME 7 www.nature.com/reviews/molcellbio © 2006 Nature Publishing Group 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 MS ES VTI EEW 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 NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 7 | MARCH 2006 | 179 © 2006 Nature Publishing Group REVIEWS 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 180 | MARCH 2006 | VOLUME 7 www.nature.com/reviews/molcellbio © 2006 Nature Publishing Group 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 MS ES VTI EEW 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 NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 7 | MARCH 2006 | 181 © 2006 Nature Publishing Group REVIEWS 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 Various gene families http://www.ambion.com Qiagen GenomeWide siRNA Various gene families 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 ~22,000 constructs targeting ~7,000 genes http://www.biomedicalgenetics.nl/ Members/Bernards/bernards.html 25 The RNAi Consortium (TRC) Lentiviral shRNA library and MISSION shRNA Human library ~60,000 constructs targeting ~13,000 genes 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 Various gene families 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, 182 | MARCH 2006 | VOLUME 7 www.nature.com/reviews/molcellbio © 2006 Nature Publishing Group 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 MS ES VTI EEW 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. NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 7 | MARCH 2006 | 183 © 2006 Nature Publishing Group 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. 184 | MARCH 2006 | VOLUME 7 www.nature.com/reviews/molcellbio © 2006 Nature Publishing Group 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 MS ES VTI EEW 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 NATURE REVIEWS | MOLECULAR CELL BIOLOGY VOLUME 7 | MARCH 2006 | 185 © 2006 Nature Publishing Group 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. 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Indeed, RNAi can be achieved locally by delivering siRNAs, shRNA-expressing plasmid DNAs or viral particles directly into the target organ71–74. Several groups have also reported the successful generation of knockdown mice by transgenic RNAi approaches. For example, plasmid shRNAs can be linearized and injected into the pronucleus of fertilized eggs that are then transferred to pseudopregnant females, or they can be electroporated into mouse embryonic stem cells that are then injected into tetraploid blastocysts or introduced by the tetraploid-aggregation method75. Alternatively, lentiviral-based shRNAs can be transduced into mouse embryonic stem cells, and the resulting clones can be assayed for transgene copy number before proceeding further76. Lentiviral shRNAs can also be directly injected into the perivitelline space of single-cell mouse embryos that are then transferred into female recipient mice77,78. Importantly, gene targeting with RNAi in other model organisms — such as rats — for which conventional knockout technologies are not available is also achievable. In vivo applications of RNAi will certainly mature in the coming years and will help to accelerate the functional annotation of the mouse genome. Inducible systems. Unregulated (that is, constitutive) RNAi technologies make it difficult to explore the functions of essential genes. Several inducible and conditional RNAi systems have been developed to overcome this problem51,76. Most of these systems rely on either the Tet repressor or the Cre recombinase. A development that might provide a definitive solution for controlling shRNA expression came from the observation that shRNAs, when engineered into an endogenous miRNA to produce an shRNA-mir, can efficiently inhibit the expression of mRNAs that contain a complementary target site21. 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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. VOLUME 7 | MARCH 2006 | 187 © 2006 Nature Publishing Group