Dataset Information

A novel method for tissue-specific RNAi rescue in Drosophila.

ABSTRACT: Targeted gene silencing by RNA interference allows the study of gene function in plants and animals. In cell culture and small animal models, genetic screens can be performed--even tissue-specifically in Drosophila--with genome-wide RNAi libraries. However, a major problem with the use of RNAi approaches is the unavoidable false-positive error caused by off-target effects. Until now, this is minimized by computational RNAi design, comparing RNAi to the mutant phenotype if known, and rescue with a presumed ortholog. The ultimate proof of specificity would be to restore expression of the same gene product in vivo. Here, we present a simple and efficient method to rescue the RNAi-mediated knockdown of two independent genes in Drosophila. By exploiting the degenerate genetic code, we generated Drosophila RNAi Escape Strategy Construct (RESC) rescue proteins containing frequent silent mismatches in the complete RNAi target sequence. RESC products were no longer efficiently silenced by RNAi in cell culture and in vivo. As a proof of principle, we rescue the RNAi-induced loss of function phenotype of the eye color gene white and tracheal defects caused by the knockdown of the heparan sulfate proteoglycan syndecan. Our data suggest that RESC is widely applicable to rescue and validate ubiquitous or tissue-specific RNAi and to perform protein structure-function analysis.

SUBMITTER: Schulz JG

PROVIDER: S-EPMC2715260 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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A novel method for tissue-specific RNAi rescue in Drosophila.

Schulz Joachim G JG David Guido G Hassan Bassem A BA

Nucleic acids research 20090529 13

Targeted gene silencing by RNA interference allows the study of gene function in plants and animals. In cell culture and small animal models, genetic screens can be performed--even tissue-specifically in Drosophila--with genome-wide RNAi libraries. However, a major problem with the use of RNAi approaches is the unavoidable false-positive error caused by off-target effects. Until now, this is minimized by computational RNAi design, comparing RNAi to the mutant phenotype if known, and rescue with ...[more]

PMID: 19483100

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Project description:RNA-sequencing (RNA-seq) is rapidly emerging as the technology of choice for whole-transcriptome studies. However, RNA-seq is not a bias free technique. It requires large amounts of RNA and library preparation can introduce multiple artifacts, compounded by problems from later stages in the process. Nevertheless, RNA-seq is increasingly used in multiple studies, including the characterization of tissue-specific transcriptomes from invertebrate models of human disease. The generation of samples in this context is complex, involving the establishment of mutant strains and the delicate contamination prone process of dissecting the target tissue. Moreover, in order to achieve the required amount of RNA, multiple samples need to be pooled. Such datasets pose extra challenges due to the large variability that may occur between similar pools, mostly due to the presence of cells from surrounding tissues. Therefore, in addition to standard quality control of RNA-seq data, analytical procedures for control of "biological quality" are critical for successful comparison of gene expression profiles. In this study, the transcriptome of the central nervous system (CNS) of a Drosophila transgenic strain with neuronal-specific RNAi of an ubiquitous gene was profiled using RNA-seq. After observing the existence of an unusual variance in our dataset, we showed that the expression profile of a small panel of marker genes, including GAL4 under control of a tissue specific driver, can identify libraries with low levels of contamination from neighboring tissues, enabling the selection of a robust dataset for differential expression analysis. We further analyzed the potential of profiling a complex tissue to identify cell-type specific changes in response to target gene down-regulation. Finally, we showed that trimming 5' ends of reads decreases nucleotide frequency biases, increasing the coverage of protein coding genes with a potential positive impact in the incurrence of systematic technical errors.

Project description:Human GWAS of obesity have been successful in identifying loci associated with adiposity, but for the most part, these are non-coding SNPs whose function, or even whose gene of action, is unknown. To help identify the genes on which these human BMI loci may be operating, we conducted a high throughput screen in Drosophila melanogaster. Starting with 78 BMI loci from two recently published GWAS meta-analyses, we identified fly orthologs of all nearby genes (± 250KB). We crossed RNAi knockdown lines of each gene with flies containing tissue-specific drivers to knock down (KD) the expression of the genes only in the brain and the fat body. We then raised the flies on a control diet and compared the amount of fat/triglyceride in the tissue-specific KD group compared to the driver-only control flies. 16 of the 78 BMI GWAS loci could not be screened with this approach, as no gene in the 500-kb region had a fly ortholog. Of the remaining 62 GWAS loci testable in the fly, we found a significant fat phenotype in the KD flies for at least one gene for 26 loci (42%) even after correcting for multiple comparisons. By contrast, the rate of significant fat phenotypes in RNAi KD found in a recent genome-wide Drosophila screen (Pospisilik et al. (2010) is ~5%. More interestingly, for 10 of the 26 positive regions, we found that the nearest gene was not the one that showed a significant phenotype in the fly. Specifically, our screen suggests that for the 10 human BMI SNPs rs11057405, rs205262, rs9925964, rs9914578, rs2287019, rs11688816, rs13107325, rs7164727, rs17724992, and rs299412, the functional genes may NOT be the nearest ones (CLIP1, C6orf106, KAT8, SMG6, QPCTL, EHBP1, SLC39A8, ADPGK /ADPGK-AS1, PGPEP1, KCTD15, respectively), but instead, the specific nearby cis genes are the functional target (namely: ZCCHC8, VPS33A, RSRC2; SPDEF, NUDT3; PAGR1; SETD1, VKORC1; SGSM2, SRR; VASP, SIX5; OTX1; BANK1; ARIH1; ELL; CHST8, respectively). The study also suggests further functional experiments to elucidate mechanism of action for genes evolutionarily conserved for fat storage.

Dataset Information

A novel method for tissue-specific RNAi rescue in Drosophila.

Publications

A novel method for tissue-specific RNAi rescue in Drosophila.

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