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. 2012 Jan 17;109(3):869-74.
doi: 10.1073/pnas.1119873109. Epub 2012 Jan 4.

Tiling Genomes of Pathogenic Viruses Identifies Potent Antiviral shRNAs and Reveals a Role for Secondary Structure in shRNA Efficacy

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Free PMC article

Tiling Genomes of Pathogenic Viruses Identifies Potent Antiviral shRNAs and Reveals a Role for Secondary Structure in shRNA Efficacy

Xu Tan et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

shRNAs can trigger effective silencing of gene expression in mammalian cells, thereby providing powerful tools for genetic studies, as well as potential therapeutic strategies. Specific shRNAs can interfere with the replication of pathogenic viruses and are currently being tested as antiviral therapies in clinical trials. However, this effort is hindered by our inability to systematically and accurately identify potent shRNAs for viral genomes. Here we apply a recently developed highly parallel sensor assay to identify potent shRNAs for HIV, hepatitis C virus (HCV), and influenza. We observe known and previously unknown sequence features that dictate shRNAs efficiency. Validation using HIV and HCV cell culture models demonstrates very high potency of the top-scoring shRNAs. Comparing our data with the secondary structure of HIV shows that shRNA efficacy is strongly affected by the secondary structure at the target RNA site. Artificially introducing secondary structure to the target site markedly reduces shRNA silencing. In addition, we observe that HCV has distinct sequence features that bias HCV-targeting shRNAs toward lower efficacy. Our results facilitate further development of shRNA based antiviral therapies and improve our understanding and ability to predict efficient shRNAs.

Conflict of interest statement

Conflict of interest statement: S.J.E., G.J.H., and S.W.L. are founders of Mirimus and serve on its scientific advisory board.

Figures

Fig. 1.
Fig. 1.
Sensor assay screen of 40,000 shRNAs targeting four viral genomes. (A) Schematic for construction of the virus sensor library. Four viral genomes were tiled using ∼40,000 shRNAs that cover almost every possible target site. This library of 210-mer oligonucleotides that cover the shRNA sequence and cognate 50-nt target sites were synthesized on one microarray chip and cloned in two steps into the pSENSOR vector. (B) FACS sorting results of the sensor assay. First row: Top and bottom shRNA controls used for determining the gatings for OnDox conditions. Second row: Top shRNA controls used for determining the gatings for OffDox conditions. Third and Fourth rows: six sorts of the library. S1, S3, S5: sorts 1, 3, 5 are OnDox conditions and sorting for the low fluorescence population; S2, S4, S6: sorts 2, 4, 6 are OffDox conditions and sorting for the high fluorescence population. x axis is fluorescence intensity and y axis is normalized cell count. (C) Log2 Sensor enrichment scores of 13 control shRNAs in the library after sort 2, sort 4, and sort 6. Strong shRNAs are highly enriched compared with weak and medium shRNAs. (D) Comparison of nucleotide frequency of top shRNAs (sensor score >4) and low-scoring shRNAs (sensor <1). The 22-nt guide strand sequence is shown in dark colors; the 14-nt flanking sequences are shown in pastel colors. The flanking sequences of the reverse complements of the mRNA target region are shown: L corresponds to the 5′ flanking region and R to the 3′ flank of that reverse-complement strand covering the target sequence. Asterisks indicate positions that have a significant difference between top and low scoring shRNAs (P < 0.01). (E) Average GC content of the 4-nt sliding window of the 22-nt target site (green background) and the flanking regions, starting from 5′ to 3′ of the mRNA target.
Fig. 2.
Fig. 2.
Validation of top-scoring shRNAs in HIV and HCV cell-culture models. (A) Validation of six top-scoring shRNAs (N1–N6) targeting different HIV NL43 genes by rtPCR after NL43 infection of HeLa-CD4 cells in comparison with three low scoring shRNAs (N7–N9) and a control shRNA targeting firefly luciferase (FF). (B) Validation of the ten top scoring shRNAs (J1–J10) targeting different HCV-JFH1 genes by immunostaining of core protein of HCV in Huh 7.5.1 cells in comparison with a low scoring shRNA (J11) and a control shRNA targeting firefly luciferase (FF). (C) Immunostaining images of a top-scoring shRNAs J9 targeting HCV and shRNA targeting firefly luciferase were shown. (Left) DAPI staining showing nuclei of Huh 7.5.1 cells; (Right) staining of HCV core protein. The numbers are the percentages of cells infected by HCV. (Maginification: 20×.) (D) Three validated top shRNAs (J6, J7, J9) and a low-scoring shRNA (J11) were synthesized as siRNAs. Transfections of the siRNAs converted from top scoring shRNAs have strong anti-HCV effect compared with the low scoring J11 and a negative control siRNA targeting firefly luciferase.
Fig. 3.
Fig. 3.
ShRNA efficacy is strongly influenced by target RNA secondary structure. (A) Correlation between the shRNA sensor score and SHAPE activity data for HIV NL43. Values are medians of a sliding window of 75 nt. The correlation coefficient is 0.3713 (P value < 10−100). Asterisks indicate regions with significant secondary structures. (B) RTS design scheme: 10-nt sequence reverse complementary to the seed-region binding site of the shRNA target site was inserted after the sensor sequence to form a duplex with the target site. The 10-nt scrambled sequences were inserted separately as negative controls. Three shRNAs targeting HIV-NL43 (N4, N6, N10) were tested using this method. (C) RTS experiments using the pSensor construct shows RTS can significantly reduce the depletion efficiency of shRNAs. Sensor scores are shown in the parentheses.
Fig. 4.
Fig. 4.
HCV-targeting shRNAs have low sensor scores in average than HIV- and influenza A virus-targeting shRNAs. (A) Sensor score distribution of shRNAs by viral strains. (B) GC content and nucleotides frequencies of the four viral strains. (C) GC content and A/U ratio plot of 55 human RNA viruses and the average of 498 top shRNAs from the sensor screen.

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