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12Design and Application of a shRNA-Based GeneReplacement RetrovirusRugang Zhang, Peter D. Adams, and Xiaofen YeSummaryTo perform structure/function analyses of a protein in vivo, ideally one should be able tosimultaneously abolish expression of the endogenous wild-type protein, substitute it with a formof the protein containing a targeted mutation, and analyze the functional consequences. Untilrecently, this was a highly challenging and/or laborious approach in mammalian systems, requiringa targeted gene knockin in a human cell line or mouse. Herein is described a RNA interference(RNAi)-based approach to achieve this much more simply in mammalian cells. A single retrovirushas been constructed, which directs expression of a short hairpin RNA (shRNA) to knockdownexpression of the endogenous protein of interest; a cDNA coding for a wild-type or mutantversion of the same protein that also contains “silent mutations” that do not affect the proteinsequence, but do make the mRNA resistant to the shRNA; and a puromycin-resistance gene toallow rapid drug selection of the virus-infected cells. Using this virus, expression of the endogenousAnti-Silencing Function 1a (ASF1a) histone chaperone has been efficiently replaced in primaryhuman cells, by an ectopically expressed epitope-tagged version. Moreover, the virus isdesigned so that other shRNA and shRNA-resistant cDNA cassettes can easily be substituted,making the approach readily applicable to other protein targets.Key Words: Gene replacement; retrovirus; shRNA; silent mutations; shRNA-resistant cDNA;U6 promoter.1. IntroductionRNAi-based technologies have revolutionized the molecular and cellularapproaches taken to understand biological processes in mammalian cells. Forexample, when studying gene function at the cellular level, researchers can useRNAi technology to quickly generate cells lacking the gene of interest andexamine its “loss-of-function” phenotype (1). Furthermore, it is becomingfeasible to perform RNAi-based “genetic screens” in mammalian cells for geneproducts whose inactivation confers a specific cellular phenotype (2–6).From: Methods in Molecular Biology, vol. 408: Gene Function AnalysisEdited by: M. Ochs © Humana Press Inc., Totowa, NJ211
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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PrefaceThis volume of Methods in Mo
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Prefaceixcolleagues demonstrate how
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xiiContentsPART III EXPERIMENTAL ME
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ICOMPUTATIONAL METHODS I
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4 BidautTable 1Input File Format Us
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6 BidautTable 2Folder Layout to Use
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8 Bidaut• alphaA: this is the num
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10 Bidautcomputing the maximum corr
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Table 3Some Identified Patterns (5,
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16 BidautFig. 4. This is a comparis
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18 BidautReferences1. Hughes, T. R.
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20 Kirov et al.way to associate gen
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22 Kirov et al.based on a study ass
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24 Kirov et al.1. Retrieve the gene
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26Fig. 1. Functional associations f
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28 Kirov et al.Fig. 2. Pathway anal
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30 Kirov et al.3. Gene symbols usag
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32 Kirov et al.9. OBO_Team, Open Bi
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3Estimating Gene Function With Leas
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Estimating Gene Function With LS-NM
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50 Gonye et al.activity and problem
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52 Gonye et al.Currently, PAINT can
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Prediction Using PAINT 57represente
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Prediction Using PAINT 59In PAINT,
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Prediction Using PAINT 6114. On the
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Prediction Using PAINT 634.2. Size
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65Fig. 4. Localization of enrichmen
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Prediction Using PAINT 673. Okubo,
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5Prediction of Intrinsic Disorder a
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Prediction of ID and Its Use in Fun
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Table 1Summary of the Web Servers O
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IICOMPUTATIONAL METHODS II
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94 Crabtree et al.genomes, which is
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96 Crabtree et al.Fig. 2. Sybil pro
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98 Crabtree et al.Fig. 3. Computing
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100 Crabtree et al.3.1.5.1. FILTER
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102 Crabtree et al.3. For the sake
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104 Crabtree et al.Fig. 5. Best bid
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106 Crabtree et al.17. Some cluster
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108 Crabtree et al.19. Chado—The
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110 Dateproducts prevents the under
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112 DateDetails of these tasks are
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114 DateThis step creates additiona
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116 Date>hsapiens|gi|20093443 >hsap
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118 DateBLAST score from the match
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Table 1A Sample of Results From Pro
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122 DateFig. 1. A network of functi
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124 Datedescribed by Verjovsky Marc
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126 Dateor contracts put forth by t
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8Bioinformatics Tools for Modeling
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Modeling Transcription Factor Targe
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VISTA Program to search for TFBSs H
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Ac 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0
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154 Osborne et al.are included in t
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156 Osborne et al.Fig. 2. Flowchart
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158 Osborne et al.UMLS source abbre
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160 Osborne et al.Fig. 3. Querying
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162 Osborne et al.3.4.2. Installati
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164 Osborne et al.amount of filteri
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166 Osborne et al.public class MMTx
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168 Osborne et al.Fig. 4. Input tes
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10Statistical Methods for Identifyi
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Identifying Differentially Expresse
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DNA Vector-Based shRNA-Expression S
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244 Hust et al.overcome by two appr
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246 Hust et al.Fig. 1. Schematic de
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248 Hust et al.interaction during p
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250 Hust et al.3.4. Titering1. Inoc
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252 Hust et al.10. Shortly before u
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254 Hust et al.activity by preservi
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15A Bacterial/Yeast Merged Two-Hybr
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Screening in Yeast With a Bacterial
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318 Thibodeau-Beganny and Joungbeen
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320 Thibodeau-Beganny and JoungFig.
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324 Thibodeau-Beganny and JoungTypi
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328 Thibodeau-Beganny and JoungPCR
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332 Thibodeau-Beganny and Joung2. P
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334 Thibodeau-Beganny and Joung11.
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336 IndexKknockin (gene knockin) 19
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