Gene silencing using RNA interference (RNAi) has become a prominent biological tool for gene annotation, pathway analysis, and target discovery in mammalian cells. High-throughput screens conducted using whole-genome siRNA libraries have uncovered rich sets of new genes involved in a variety of biological processes and cellular models of disease. However, high-throughput RNAi screening is not yet a mainstream tool in life science research because current screening platforms are expensive and onerous. Miniaturizing the RNAi screening platform to reduce cost and increase throughput will enable its widespread use and harness its potential for rapid genome annotation. With this aim, we have combined semi-conductor microfabrication and nanolitre dispensing techniques to develop miniaturized electroporation-ready microwell arrays loaded with siRNA molecules in which multiplexed gene knockdown can be achieved. Arrays of microwells are created using high-aspect ratio biocompatible photoresists on optically transparent and conductive Indium-Tin Oxide (ITO) substrates with integrated micro-electrodes to enable in situ electroporation. Non-contact inkjet microarraying allows precise dispensing of nanolitre volumes into the microwell structures. We have achieved parallel electroporation of multiple mammalian cells cultured in these microwell arrays and observed efficient knockdown of genes with surface-bound, printed siRNAs. Further integration of microfabrication and non-contact nanolitre dispensing techniques described here may enable single-substrate whole-genome siRNA screening in mammalian cells.
In order to identify novel targets in pancreatic cancer cells, we utilized high-throughput RNAi (HT-RNAi) to identify genes that, when silenced, would decrease viability of pancreatic cancer cells. The HT-RNAi screen involved reverse transfecting the pancreatic cancer cell line BxPC3 with a siRNA library targeting 572 kinases. From replicate screens, approximately thirty-two kinases were designated as hits, of which twenty-two kinase targets were selected for confirmation and validation. One kinase identified as a hit from this screen was tyrosine kinase non-receptor 1 (TNK1), a kinase previously identified as having tumor suppressor-like properties in embryonic stem cells. Silencing of TNK1 with siRNA showed reduced proliferation in a panel of pancreatic cancer cell lines. Furthermore, we demonstrated that silencing of TNK1 led to increased apoptosis through a caspase-dependent pathway and that targeting TNK1 with siRNA can synergize with gemcitabine treatment. Despite previous reports that TNK1 affects Ras and NFκB signaling, we did not find similar correlations with these pathways in pancreatic cancer cells. Our results suggest that TNK1 in pancreatic cancer cells does not possess the same tumor suppressor properties seen in embryonic cells, but appears to be involved in growth and survival. The application of functional genomics using HT-RNAi screens has allowed us to identify TNK1 as a growth-associated kinase in pancreatic cancer cells.
HT-RNAi; TNK1; gemcitabine; pancreatic cancer
Short interfering RNA (siRNA) induced RNA interference (RNAi) responses allow for discovery research and performing large scale screening1-5; however, due to their size and anionic charge, siRNAs have no bioavailability to enter cells4,5. Current approaches fail to deliver siRNAs into a high percentage of primary cells in a non-cytotoxic fashion. Here we report an efficient siRNA delivery approach that utilizes a Peptide Transduction Domain-dsRNA Binding Domain (PTD-DRBD) fusion protein. DRBDs bind to siRNAs with high avidity, masking the siRNA negative charge and allow for PTD-mediated cellular uptake. PTD-DRBD delivered siRNAs induced rapid RNAi responses in a non-cytotoxic manner in the entire cell population of primary and transformed cells, including T cells, HUVECs and hESCs. Whole genome microarray analysis showed minimal transcriptional changes by PTD-DRBD and we did not detect any innate immune responses in PBMCs. Thus, PTD-DRBD mediated siRNA delivery allows efficient RNAi manipulation of difficult primary cell types.
RNA interference (RNAi) is commonly applied in genome-scale gene functional screens. However, a one-on-one RNAi analysis that targets each gene is cost-ineffective and laborious. Previous studies have indicated that siRNAs can also affect RNAs that are near-perfectly complementary, and this phenomenon has been termed an off-target effect. This phenomenon implies that it is possible to silence several genes simultaneously with a carefully designed siRNA.
We propose a strategy that is combined with a heuristic algorithm to design suitable siRNAs that can target multiple genes and a group testing method that would reduce the number of required RNAi experiments in a large-scale RNAi analysis. To verify the efficacy of our strategy, we used the Orchid expressed sequence tag data as a case study to screen the putative transcription factors that are involved in plant disease responses. According to our computation, 94 qualified siRNAs were sufficient to examine all of the predicated 229 transcription factors. In addition, among the 94 computer-designed siRNAs, an siRNA that targets both TF15 (a previously identified transcription factor that is involved in the plant disease-response pathway) and TF21 was introduced into orchids. The experimental results showed that this siRNA can simultaneously silence TF15 and TF21, and application of our strategy successfully confirmed that TF15 is involved in plant defense responses. Interestingly, our second-round analysis, which used an siRNA specific to TF21, indicated that TF21 is a previously unidentified transcription factor that is related to plant defense responses.
Our computational results showed that it is possible to screen all genes with fewer experiments than would be required for the traditional one-on-one RNAi screening. We also verified that our strategy is capable of identifying genes that are involved in a specific phenotype.
RNA interference; RNAi screening; SiRNA design; Gene functional analysis; Group testing
The completion of the genome sequencing for several organisms has
created a great demand for genomic tools that can systematically
analyze the growing wealth of data. In contrast to the classical
reverse genetics approach of creating specific knockout cell lines
or animals that is time-consuming and expensive, RNA-mediated
interference (RNAi) has emerged as a fast, simple, and
cost-effective technique for gene knockdown in large scale. Since
its discovery as a gene silencing response to double-stranded RNA
(dsRNA) with homology to endogenous genes in
Caenorhabditis elegans (C elegans),
RNAi technology has been adapted to various high-throughput
screens (HTS) for genome-wide loss-of-function (LOF) analysis.
Biochemical insights into the endogenous mechanism of
RNAi have led to advances in RNAi methodology including RNAi
molecule synthesis, delivery, and sequence design. In this
article, we will briefly review these various RNAi library designs
and discuss the benefits and drawbacks of each library strategy.
RNA interference (RNAi) has opened promising avenues to better understand gene function. Though many RNAi screens report on the identification of genes, very few, if any, have been further studied and validated. Data discrepancy is emerging as one of RNAi main pitfalls. We reasoned that a systematic analysis of lethality-based screens, since they score for cell death, would examine the extent of hit discordance at inter-screen level. To this end, we developed a methodology for literature mining and overlap analysis of several screens using both siRNA and shRNA flavors, and obtained 64 gene lists censoring an initial list of 7,430 nominated genes. We further performed a comparative analysis first at a global level followed by hit re-assessment under much more stringent conditions. To our surprise, none of the hits overlapped across the board even for PLK1, which emerged as a strong candidate in siRNA screens; but only marginally in the shRNA ones. Furthermore, EIF5B emerges as the most common hit only in the shRNA screens. A highly unusual and unprecedented result was the observation that 5,269 out of 6,664 nominated genes (~80%) in the shRNA screens were exclusive to the pooled format, raising concerns as to the merits of pooled screens which qualify hits based on relative depletions, possibly due to multiple integrations per cell, data deconvolution or inaccuracies in intracellular processing causing off-target effects. Without golden standards in place, we would encourage the community to pay more attention to RNAi screening data analysis practices, bearing in mind that it is combinatorial in nature and one active siRNA duplex or shRNA hairpin per gene does not suffice credible hit nomination. Finally, we also would like to caution interpretation of pooled shRNA screening outcomes.
RNAi; shRNA; siRNA; Gene; screening; bioinformatics; analysis; overlap; lethality; essential; PLK1
RNA interference (RNAi) has been seen as a revolution in functional genomics and system biology. Genome-wide RNAi research relies on the development of RNAi high-throughput screening (HTS) assays. One of the most fundamental challenges in RNAi HTS is to glean biological significance from mounds of data, which relies on the development of effective analytic methods for selecting effective small interfering RNAs (siRNAs).
Based on a recently proposed parameter, strictly standardized mean difference (SSMD), I propose an analytic method for genome-wide screens of effective siRNAs through assessing and testing the size of siRNA effects. Central to this method is the capability of SSMD in quantifying siRNA effects. This method has relied on normal approximation, which works only in the primary screens but not in the confirmatory screens. In this paper, I explore the non-central t-distribution property of SSMD estimates and use this property to extend the SSMD-based method so that it works effectively in either primary or confirmatory screens as well as in any HTS screens with or without replicates. The SSMD-based method maintains a balanced control of false positives and false negatives.
The central interest in genome-wide RNAi research is the selection of effective siRNAs which relies on the development of analytic methods to measure the size of siRNA effects. The new analytic method for hit selection provided in this paper offers a good analytic tool for selecting effective siRNAs, better than current analytic methods, and thus may have broad utility in genome-wide RNAi research.
RNA interference (RNAi) is a process in which double-strand RNA (dsRNA) directs the specific degradation of a corresponding target mRNA. The mediators of this process are small dsRNAs, of ∼21 bp in length, called small interfering RNAs (siRNAs). siRNAs, which can be prepared in vitro in a number of ways and then transfected into cells, can direct the degradation of corresponding mRNAs inside these cells. Hence, siRNAs represent a powerful tool for studying gene functions, as well as having the potential of being highly specific pharmaceutical agents. Some limitations in using this technology exist because the preparation of siRNA in vitro and screening for siRNAs efficient in RNAi can be expensive and time-consuming processes. Here, we demonstrate that custom oligonucleotide arrays can be efficiently used for the preparation of defined mixtures of siRNAs for the silencing of exogenous and endogenous genes. The method is fast, inexpensive, does not require siRNA optimization and has a number of advantages over methods utilizing enzymatic preparation of siRNAs by digestion of longer dsRNAs, as well as methods based on chemical synthesis of individual siRNAs or their DNA templates.
High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.
The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.
With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.
RNA interference (RNAi) is a process of post-transcriptional gene silencing initiated by double-stranded RNAs, including short interfering RNA (siRNA). Silencing is sequence-specific and RNAi has rapidly become central to the study of gene function. RNAi also carries promise for selective silencing of viral and endogenous genes causal for disease. To detect the very low levels of siRNA effective for RNAi we modified the 3′ end of the sense strand of siRNA with a nuclease-resistant DNA hairpin. We show that the modified siRNA-DNA construct (termed ‘crook’ siRNA) functions as a primer for the PCR and describe a novel, yet simple PCR protocol for its quantification (amolar levels/cell). When transfected into mammalian cells, crook siRNA induces selective mRNA knock-down equivalent to its unmodified siRNA counterpart. This new bifunctional siRNA construct will enable future in vivo studies on the uptake, distribution and pharmacokinetics of siRNA, and is particularly important for the development of siRNA-based therapeutics. More generally, PCR-based detection of siRNA carries wide-ranging applications for RNAi reverse genetics.
Meaningful RNAi-based data for target gene identification are strongly dependent on the use of a biologically relevant cell type and efficient delivery of highly functional siRNA reagents into the selected cell type. Here we report the use of the Amaxa® Nucleofector® 96-well Shuttle® System for siRNA screening in primary cells. Lonza's Clonetics® HUVEC-Human Umbilical Vein Endothelial Cells were transfected with Thermo Scientific Dharmacon siGENOME® siRNA Libraries targeting protein kinases and cell cycle related genes and screened for genes important for cell viability. Of the 37 primary hits, down-regulation of 33 led to reduced proliferation or increased cell death, while down-regulation of two allowed for better cell viability. The validated four genes out of the 16 strongest primary hits (COPB2, PYCS, CDK4 and MYC) influenced cell proliferation to varying degrees, reflecting differing importance for survival of HUVEC cells. Our results demonstrate that the Nucleofector® 96-well Shuttle® System allows the delivery of siRNA libraries in cell types previously considered to be difficult to transfect. Thus, identification and validation of gene targets can now be conducted in primary cells, as the selection of cell types is not limited to those accessible by lipid-mediated transfection.
Nucleofection; RNAi; siRNA; primary cell; screening; transfection; HUVEC
Short interfering RNAs (siRNAs) are widely used to bring about RNA
interference (RNAi) in mammalian cells. Numerous siRNAs may be
designed for any target gene though most of which would be
incapable of efficiently inducing mammalian RNAi. Certain highly
functional siRNAs designed for knockout of a particular gene may
render unrelated endogenous genes nonfunctional. These major
bottlenecks should be properly eliminated when RNAi technologies
are employed for any experiment in mammalian functional genomics.
This paper thus presents essential notes and findings regarding
the proper choice of siRNA-sequence selection algorithms and
web-based online software systems.
RNA interference (RNAi) is a cellular mechanism in which a short/small double stranded RNA induces the degradation of its sequence specific target mRNA, leading to specific gene silencing. Since its discovery, RNAi has become a powerful biological technique for gene function studies and drug discovery. The very first requirement of applying RNAi is to design functional small interfering RNA (siRNA) that can uniquely induce the degradation of the targeted mRNA. It has been shown that many functional synthetic siRNAs share some common characteristics, such as GC content limitation and free energy preferences at both terminals, etc.
Our three-phase algorithm was developed to design siRNA on a whole-genome scale based on those identified characteristics of functional siRNA. When this algorithm was applied to design short hairpin RNA (shRNA), the validated success rate of shRNAs was over 70%, which was almost double the rate reported for TRC library. This indicates that the designs of siRNA and shRNA may share the same concerns. Further analysis of the shRNA dataset of 444 designs reveals that the high free energy states of the two terminals have the largest positive impact on the shRNA efficacy. Enforcing these energy characteristics of both terminals can further improve the shRNA design success rate to 83.1%. We also found that functional shRNAs have less probability for their 3' terminals to be involved in mRNA secondary structure formation.
Functional shRNAs prefer high free energy states at both terminals. High free energy states of the two terminals were found to be the largest positive impact factor on shRNA efficacy. In addition, the accessibility of the 3' terminal is another key factor to shRNA efficacy.
RNA interference (RNAi) has become a powerful means for silencing target gene expression in mammalian cells and is envisioned to be useful in therapeutic approaches to human disease. In recent years, high-throughput, genome-wide screening of siRNA/miRNA libraries has emerged as a desirable approach. Current methods for constructing siRNA/miRNA expression vectors require the synthesis of long oligonucleotides, which is costly and suffers from mutation problems.
Here we report an ingenious method to solve traditional problems associated with construction of siRNA/miRNA expression vectors. We synthesized shorter primers (< 50 nucleotides) to generate a linear expression structure by PCR. The PCR products were directly transformed into chemically competent E. coli and converted to functional vectors in vivo via homologous recombination. The positive clones could be easily screened under UV light. Using this method we successfully constructed over 500 functional siRNA/miRNA expression vectors. Sequencing of the vectors confirmed a high accuracy rate.
This novel, convenient, low-cost and highly efficient approach may be useful for high-throughput assays of RNAi libraries.
A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.
We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.
PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.
In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.
This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.
RNA interference (RNAi) provides a powerful new means to inhibit viral infection specifically. However, the selection of siRNA-resistant viruses is a major concern in the use of RNAi as antiviral therapeutics. In this study, we conducted a lentiviral vector with a H1-short hairpin RNA (shRNA) expression cassette to deliver small interfering RNAs (siRNAs) into mammalian cells. Using this vector that also expresses enhanced green fluorescence protein (EGFP) as surrogate marker, stable shRNA-expressing cell lines were successfully established and the inhibition efficiencies of rationally designed siRNAs targeting to conserved regions of influenza A virus genome were assessed. The results showed that a siRNA targeting influenza M2 gene (siM2) potently inhibited viral replication. The siM2 was not only effective for H1N1 virus but also for highly pathogenic avian influenza virus H5N1. In addition to its M2 inhibition, the siM2 also inhibited NP mRNA accumulation and protein expression. A long term inhibition effect of the siM2 was demonstrated and the emergence of siRNA-resistant mutants in influenza quasispecies was not observed. Taken together, our study suggested that M2 gene might be an optimal RNAi target for antiviral therapy. These findings provide useful information for the development of RNAi-based prophylaxis and therapy for human influenza virus infection.
RNA interference-based screening is a powerful new genomic technology which addresses gene function en masse. To evaluate factors influencing hit list composition and reproducibility, we performed two identically designed small interfering RNA (siRNA)-based, whole genome screens for host factors supporting yellow fever virus infection. These screens represent two separate experiments completed five months apart and allow the direct assessment of the reproducibility of a given siRNA technology when performed in the same environment. Candidate hit lists generated by sum rank, median absolute deviation, z-score, and strictly standardized mean difference were compared within and between whole genome screens. Application of these analysis methodologies within a single screening dataset using a fixed threshold equivalent to a p-value ≤ 0.001 resulted in hit lists ranging from 82 to 1,140 members and highlighted the tremendous impact analysis methodology has on hit list composition. Intra- and inter-screen reproducibility was significantly influenced by the analysis methodology and ranged from 32% to 99%. This study also highlighted the power of testing at least two independent siRNAs for each gene product in primary screens. To facilitate validation we conclude by suggesting methods to reduce false discovery at the primary screening stage.
In this study we present the first comprehensive comparison of multiple analysis strategies, and demonstrate the impact of the analysis methodology on the composition of the “hit list”. Therefore, we propose that the entire dataset derived from functional genome-scale screens, especially if publicly funded, should be made available as is done with data derived from gene expression and genome-wide association studies.
RNA interference; analysis; RNAi screen analysis; siRNA; RNAi; siRNA screening; sum rank; median absolute deviation; strictly standardized mean difference; genome-wide; whole-genome; comparison; overlap; hit list
RNA interference (RNAi) is a powerful method for specific gene
silencing which may also lead to promising novel therapeutic
strategies. It is mediated through small interfering RNAs (siRNAs)
which sequence-specifically trigger the cleavage and subsequent
degradation of their target mRNA. One critical factor is the
ability to deliver intact siRNAs into target cells/organs in vivo.
This review highlights the mechanism of RNAi and the guidelines
for the design of optimal siRNAs. It gives an overview of studies
based on the systemic or local application of naked siRNAs or the
use of various nonviral siRNA delivery systems. One promising
avenue is the the complexation of siRNAs with the polyethylenimine
(PEI), which efficiently stabilizes siRNAs and, upon systemic
administration, leads to the delivery of the intact siRNAs into
different organs. The antitumorigenic effects of
PEI/siRNA-mediated in vivo gene-targeting of tumor-relevant
proteins like in mouse tumor xenograft models are described.
Gene knock down by RNAi is a highly effective approach to silence gene expression in experimental as well as therapeutic settings. However, this widely used methodology entails serious pitfalls, especially concerning specificity of the RNAi molecules.
We tested the most widely used control siRNA directed against GFP for off-target effects and found that it deregulates in addition to GFP a set of endogenous target genes. The off-target effects were dependent on the amount of GFP siRNA transfected and were detected in a variety of cell lines. Since the respective siRNA molecule specific for GFP is widely used as negative control for RNAi experiments, we studied the complete set of off-target genes of this molecule by genome-wide expression profiling. The detected modulated mRNAs had target sequences homologous to the siRNA as small as 8 basepairs in size. However, we found no restriction of sequence homology to 3'UTR of target genes.
We can show that even siRNAs without a physiological target have sequence-specific off-target effects in mammalian cells. Furthermore, our analysis defines the off-target genes affected by the siRNA that is commonly used as negative control and directed against GFP. Since off-target effects can hardly be avoided, the best strategy is to identify false positives and exclude them from the results. To this end, we provide the set of false positive genes deregulated by the commonly used GFP siRNA as a reference resource for future siRNA experiments.
RNA interference (RNAi) leads to sequence-specific knockdown of gene function. The approach can be used in large-scale screens to interrogate function in various model organisms and an increasing number of other species. Genome-scale RNAi screens are routinely performed in cultured or primary cells or in vivo in organisms such as C. elegans. High-throughput RNAi screening is benefitting from the development of sophisticated new instrumentation and software tools for collecting and analyzing data, including high-content image data. The results of large-scale RNAi screens have already proved useful, leading to new understandings of gene function relevant to topics such as infection, cancer, obesity and aging. Nevertheless, important caveats apply and should be taken into consideration when developing or interpreting RNAi screens. Some level of false discovery is inherent to high-throughput approaches and specific to RNAi screens, false discovery due to off-target effects (OTEs) of RNAi reagents remains a problem. The need to improve our ability to use RNAi to elucidate gene function at large scale and in additional systems continues to be addressed through improved RNAi library design, development of innovative computational and analysis tools and other approaches.
RNAi; high-throughput screens; high-content imaging; cell-based assays
RNA inhibition by siRNAs is a frequently used approach to identify genes required for specific biological processes. However RNAi screening using siRNAs is hampered by non-specific or off target effects of the siRNAs, making it difficult to separate genuine hits from false positives. It is thought that many of the off-target effects seen in RNAi experiments are due to siRNAs acting as microRNAs (miRNAs), causing a reduction in gene expression of unintended targets via matches to the 6 or 7 nt 'seed' sequence. We have conducted a careful examination of off-target effects during an siRNA screen for novel regulators of the TRAIL apoptosis induction pathway(s).
We identified 3 hexamers and 3 heptamer seed sequences that appeared multiple times in the top twenty siRNAs in the TRAIL apoptosis screen. Using a novel statistical enrichment approach, we systematically identified a further 17 hexamer and 13 heptamer seed sequences enriched in high scoring siRNAs. The presence of one of these seeds sequences (which could explain 6 of 8 confirmed off-target effects) is sufficient to elicit a phenotype. Three of these seed sequences appear in the human miRNAs miR-26a, miR-145 and miR-384. Transfection of mimics of these miRNAs protects several cell types from TRAIL-induced cell death.
We have demonstrated a role for miR-26a, miR-145 and miR-26a in TRAIL-induced apoptosis. Further these results show that RNAi screening enriches for siRNAs with relevant off-target effects. Some of these effects can be identified by the over-representation of certain seed sequences in high-scoring siRNAs and we demonstrate the usefulness of such systematic analysis of enriched seed sequences.
High-throughput RNA interference (RNAi) screens have been used to find genes that, when silenced, result in sensitivity to certain chemotherapy drugs. Researchers therefore can further identify drug-sensitive targets and novel drug combinations that sensitize cancer cells to chemotherapeutic drugs. Considerable uncertainty exists about the efficiency and accuracy of statistical approaches used for RNAi hit selection in drug sensitivity studies. Researchers require statistical methods suitable for analyzing high-throughput RNAi screening data that will reduce false-positive and false-negative rates.
In this study, we carried out a simulation study to evaluate four types of statistical approaches (fold-change/ratio, parametric tests/statistics, sensitivity index, and linear models) with different scenarios of RNAi screenings for drug sensitivity studies. With the simulated datasets, the linear model resulted in significantly lower false-negative and false-positive rates. Based on the results of the simulation study, we then make recommendations of statistical analysis methods for high-throughput RNAi screening data in different scenarios. We assessed promising methods using real data from a loss-of-function RNAi screen to identify hits that modulate paclitaxel sensitivity in breast cancer cells. High-confidence hits with specific inhibitors were further analyzed for their ability to inhibit breast cancer cell growth. Our analysis identified a number of gene targets with inhibitors known to enhance paclitaxel sensitivity, suggesting other genes identified may merit further investigation.
RNAi screening can identify druggable targets and novel drug combinations that can sensitize cancer cells to chemotherapeutic drugs. However, applying an inappropriate statistical method or model to the RNAi screening data will result in decreased power to detect the true hits and increase false positive and false negative rates, leading researchers to draw incorrect conclusions. In this paper, we make recommendations to enable more objective selection of statistical analysis methods for high-throughput RNAi screening data.
The aim of the present study was to detect the expression of survivin in human lung cancer and to investigate the influence of its downregulation on the biological behavior of A549 lung cancer cells. The high expression of survivin in human lung cancer was verified by immunohistochemistry. Survivin small interfering RNA (siRNA) and unrelated sequence were synthesized and the siRNA lentiviral vector was constructed. The vector was transfected into A549 lung cancer cells, in which the clone with stable expression was screened out. We blocked the expression of survivin mRNA and protein by RNA interference (RNAi) technique. The downregulation of survivin mRNA and protein expression was confirmed by real-time quantitative PCR and western blotting. The proliferative activity and growth rate of A549 cells were determined by colony formation assay and mononuclear cell direct cytotoxicity assay (MTT assay). The reconstituted basement membrane (RBM) penetrating capacity was determined by cell invasion assay. The cell movement and migratory capacity were detected by wound-healing repair assay. The results showed that the sequence-specific siRNA significantly downregulated the expression of survivin at both the mRNA and protein levels. Downregulation of survivin expression dramatically decreased the invasive and metastatic capacities of the cells, suppressed the proliferation, decelerated the rate of growth, reduced the number of clones on soft agar and decreased the capacity of RBM penetration and migration. In conclusion, survivin, which plays an important role in carcinogenesis and development of lung cancer, can be effectively downregulated using the RNAi technique.
survivin; RNA interference; lung cancer; biological behavior
Background: RNA interference (RNAi) is the mechanism of gene silencing-mediated messenger RNA degradation by small interference RNA (siRNA), which becomes a powerful tool for in vivo research, especially in the areas of cancer. In this research, the potential use of an expression vector as a specific siRNA producing tool for silencing of Bcr-abl in K562 cell line has been investigated. Methods: siRNA specific for Bcr-abl as short hairpin RNA (shRNA) was designed and cloned in expression vector (pRNAH1.1/Neo). K562 cells were cultured in RPMI media and transfected with shRNA expressing vector using lipofectamin 2000. Successful transfection was confirmed by significant increase of enhanced green fluorescent protein (EGFP) levels in K562-treated cells with expression vector (pEGFP-C1). In vitro studies in human K562 cell line entailed modulation of endogenous Bcr-abl mRNA levels which induced apoptosis. Effects of siRNA treatment on K562 cells were measured by ELISA. Results: Successful expression of siRNA was confirmed by significant reduction of Bcr-abl mRNA levels in K562 cells treated with expression vector (pRNAH1.1/Neo). siRNA directed against Bcr-abl effectively induced apoptosis and reduced viability in human K562 cell lines. Conclusion: Expression vector of siRNA can be used in vitro to target specific RNA and to reduce the levels of the specific gene product in the targeted cells. Results of this work suggest that RNAi has potential application for the treatment of a variety of diseases, including those involving abnormal gene expression and viral contamination.
Apoptosis; SiRNA; K562 cell line; Bcr-abl