Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments

Searching for novel cell cycle regulators in Trypanosoma brucei with an RNA interference screen

<b>Background</b><br /> The protozoan parasite, Trypanosoma brucei, is spread by the tsetse fly and causes Human African Trypanosomiasis. Its cell cycle is complex and not fully understood at the molecular level. The T. brucei genome contains over 6000 protein coding genes with >50% having no predicted function. A small scale RNA interference (RNAi) screen was carried out in Trypanosoma brucei to evaluate the prospects for identifying novel cycle regulators.<p></p> <b>Results</b><br /> Procyclic form T. brucei were transfected with a genomic RNAi library and 204 clones isolated. However, only 76 RNAi clones were found to target a protein coding gene of potential interest. These clones were screened for defects in proliferation and cell cycle progression following RNAi induction. Sixteen clones exhibited proliferation defects upon RNAi induction, with eight clones displaying potential cell cycle defects. To confirm the phenotypes, new RNAi cell lines were generated and characterised for five genes targeted in these clones. While we confirmed that the targeted genes are essential for proliferation, we were unable to unambiguously classify them as cell cycle regulators.<p></p> <b>Conclusion</b><br /> Our study identified genes essential for proliferation, but did not, as hoped, identify novel cell cycle regulators. Screening of the RNAi library for essential genes was extremely labour-intensive, which was compounded by the suboptimal quality of the library. For such a screening method to be viable for a large scale or genome wide screen, a new, significantly improved RNAi library will be required, and automated phenotyping approaches will need to be incorporated.<p></p&gt

A Bayesian measurement error model for two-channel cell-based RNAi data with replicates

RNA interference (RNAi) is an endogenous cellular process in which small double-stranded RNAs lead to the destruction of mRNAs with complementary nucleoside sequence. With the production of RNAi libraries, large-scale RNAi screening in human cells can be conducted to identify unknown genes involved in a biological pathway. One challenge researchers face is how to deal with the multiple testing issue and the related false positive rate (FDR) and false negative rate (FNR). This paper proposes a Bayesian hierarchical measurement error model for the analysis of data from a two-channel RNAi high-throughput experiment with replicates, in which both the activity of a particular biological pathway and cell viability are monitored and the goal is to identify short hair-pin RNAs (shRNAs) that affect the pathway activity without affecting cell activity. Simulation studies demonstrate the flexibility and robustness of the Bayesian method and the benefits of having replicates in the experiment. This method is illustrated through analyzing the data from a RNAi high-throughput screening that searches for cellular factors affecting HCV replication without affecting cell viability; comparisons of the results from this HCV study and some of those reported in the literature are included.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS496 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A fruitful fly forward : the role of the fly in drug discovery for neurodegeneration

AD, Alzheimer’s disease; APP, amyloid precursor protein; BBB, blood brain barrier; GFP, green fluorescent protein; HTS, high-throughput screening; HD, Huntington’s disease; LB, Lewy bodies; PD, Parkinson’s disease; PolyQ, Polyglutamine; RNAi, RNA interference; SNCA, α-synuclein gene; UAS, Upstream Activating Sequence.peer-reviewe

A network-based integrative approach to prioritize reliable hits from multiple genome-wide RNAi screens in Drosophila

<p>Abstract</p> <p>Background</p> <p>The recently developed RNA interference (RNAi) technology has created an unprecedented opportunity which allows the function of individual genes in whole organisms or cell lines to be interrogated at genome-wide scale. However, multiple issues, such as off-target effects or low efficacies in knocking down certain genes, have produced RNAi screening results that are often noisy and that potentially yield both high rates of false positives and false negatives. Therefore, integrating RNAi screening results with other information, such as protein-protein interaction (PPI), may help to address these issues.</p> <p>Results</p> <p>By analyzing 24 genome-wide RNAi screens interrogating various biological processes in <it>Drosophila</it>, we found that RNAi positive hits were significantly more connected to each other when analyzed within a protein-protein interaction network, as opposed to random cases, for nearly all screens. Based on this finding, we developed a network-based approach to identify false positives (FPs) and false negatives (FNs) in these screening results. This approach relied on a scoring function, which we termed NePhe, to integrate information obtained from both PPI network and RNAi screening results. Using a novel rank-based test, we compared the performance of different NePhe scoring functions and found that diffusion kernel-based methods generally outperformed others, such as direct neighbor-based methods. Using two genome-wide RNAi screens as examples, we validated our approach extensively from multiple aspects. We prioritized hits in the original screens that were more likely to be reproduced by the validation screen and recovered potential FNs whose involvements in the biological process were suggested by previous knowledge and mutant phenotypes. Finally, we demonstrated that the NePhe scoring system helped to biologically interpret RNAi results at the module level.</p> <p>Conclusion</p> <p>By comprehensively analyzing multiple genome-wide RNAi screens, we conclude that network information can be effectively integrated with RNAi results to produce suggestive FPs and FNs, and to bring biological insight to the screening results.</p

An analog of glibenclamide selectively enhances autophagic degradation of misfolded α1-antitrypsin Z

The classical form of α1-antitrypsin deficiency (ATD) is characterized by intracellular accumulation of the misfolded variant α1-antitrypsin Z (ATZ) and severe liver disease in some of the affected individuals. In this study, we investigated the possibility of discovering novel therapeutic agents that would reduce ATZ accumulation by interrogating a C. elegans model of ATD with high-content genome-wide RNAi screening and computational systems pharmacology strategies. The RNAi screening was utilized to identify genes that modify the intracellular accumulation of ATZ and a novel computational pipeline was developed to make high confidence predictions on repurposable drugs. This approach identified glibenclamide (GLB), a sulfonylurea drug that has been used broadly in clinical medicine as an oral hypoglycemic agent. Here we show that GLB promotes autophagic degradation of misfolded ATZ in mammalian cell line models of ATD. Furthermore, an analog of GLB reduces hepatic ATZ accumulation and hepatic fibrosis in a mouse model in vivo without affecting blood glucose or insulin levels. These results provide support for a drug discovery strategy using simple organisms as human disease models combined with genetic and computational screening methods. They also show that GLB and/or at least one of its analogs can be immediately tested to arrest the progression of human ATD liver disease.</div

Signaling Network Assessment of Mutations and Copy Number Variations Predicts Breast Cancer Subtype-specific Drug Targets

Individual cancer cells carry a bewildering number of distinct genomic alterations i.e., copy number variations and mutations, making it a challenge to uncover genomic-driven mechanisms governing tumorigenesis. Here we performed exome-sequencing on several breast cancer cell lines which represent two subtypes, luminal and basal. We integrated this sequencing data, and functional RNAi screening data (i.e., for identifying genes which are essential for cell proliferation and survival), onto a human signaling network. Two subtype-specific networks were identified, which potentially represent core-signaling mechanisms underlying tumorigenesis. Within both networks, we found that genes were differentially affected in different cell lines; i.e., in some cell lines a gene was identified through RNAi screening whereas in others it was genomically altered. Interestingly, we found that highly connected network genes could be used to correctly classify breast tumors into subtypes based on genomic alterations. Further, the networks effectively predicted subtype-specific drug targets, which were experimentally validated.Comment: 4 figs, more related papers at http://www.cancer-systemsbiology.org, appears in Cell Reports, 201

Combined flow cytometry and high-throughput image analysis for the study of essential genes in Caenorhabditis elegans

Background: Advances in automated image-based microscopy platforms coupled with high-throughput liquid workflows have facilitated the design of large-scale screens utilising multicellular model organisms such as Caenorhabditis elegans to identify genetic interactions, therapeutic drugs or disease modifiers. However, the analysis of essential genes has lagged behind because lethal or sterile mutations pose a bottleneck for high-throughput approaches, and a systematic way to analyse genetic interactions of essential genes in multicellular organisms has been lacking. Results: In C. elegans, non-conditional lethal mutations can be maintained in heterozygosity using chromosome balancers, commonly expressing green fluorescent protein (GFP) in the pharynx. However, gene expression or function is typically monitored by the use of fluorescent reporters marked with the same fluorophore, presenting a challenge to sort worm populations of interest, particularly at early larval stages. Here, we develop a sorting strategy capable of selecting homozygous mutants carrying a GFP stress reporter from GFP-balanced animals at the second larval stage. Because sorting is not completely error-free, we develop an automated high-throughput image analysis protocol that identifies and discards animals carrying the chromosome balancer. We demonstrate the experimental usefulness of combining sorting of homozygous lethal mutants and automated image analysis in a functional genomic RNA interference (RNAi) screen for genes that genetically interact with mitochondrial prohibitin (PHB). Lack of PHB results in embryonic lethality, while homozygous PHB deletion mutants develop into sterile adults due to maternal contribution and strongly induce the mitochondrial unfolded protein response (UPR mt ). In a chromosome-wide RNAi screen for C. elegans genes having human orthologues, we uncover both known and new PHB genetic interactors affecting the UPR mt and growth. Conclusions: The method presented here allows the study of balanced lethal mutations in a high-throughput manner. It can be easily adapted depending on the user's requirements and should serve as a useful resource for the C. elegans community for probing new biological aspects of essential nematode genes as well as the generation of more comprehensive genetic networks.European Research Council ERC-2011-StG-281691Ministerio de Economía y Competitividad BFU2012–3550

Screening for the optimal siRNA targeting a novel gene (HA117) and construction and evaluation of a derivative recombinant adenovirus

We found a novel gene named as HA117 in our previous research. At this study, we screened for an optimal siRNA targeting the novel gene HA117 using the pSOS-HUS method, verified the results of pSOS-HUS siRNA screening for optimal affinity for the target gene, and constructed and evaluated a recombinant adenovirus carrying the DNA template for transcription of the optimal HA117 siRNA. The pSOS-HUS vector method was successfully utilized as a rapid and effective screen for an optimal siRNA for a target gene. Among five pairs of DNA templates, siRNA transcribed from HAi5 gave the strongest interference with the novel gene HA117; a HAi5-carrying recombinant adenovirus (Ad-HAi5) was successfully constructed and evaluated, laying a foundation for the further study of HA117 gene function with RNAi technology