Journal of Neuroscience Methods 177 (2009) 322–333
Contents lists available at ScienceDirect
Journal of Neuroscience Metho...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 323
the contribution of MTGR1 (myeloid translo...
324 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333
were analyzed on a 1.5% agarose gel in Tri...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 325
2.5.1. Rescue of integrated provirus and i...
326 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333
Fig. 2. Retroviral transduction and delive...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 327
well of the plate. From these images, neur...
328 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333
Fig. 3. Retinoic acid (RA) induces neurite...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 329
Fig. 4. Transfection of SH-SY5Y cells with...
330 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333
Fig. 6. Overexpression of full length MTGR...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 331
Fig. 8. Lentiviral-based siRNA screening. ...
332 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333
analysis. Most importantly, we identified a...
V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 333
an alpha-, beta-, and zeta-protein kinase ...
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Loss of function genetic screens reveal MTGR1 as - Journal of ...

  1. 1. Journal of Neuroscience Methods 177 (2009) 322–333 Contents lists available at ScienceDirect Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth Loss of function genetic screens reveal MTGR1 as an intracellular repressor of ␤1 integrin-dependent neurite outgrowth Valeria S. Ossovskayaa,b,d,∗ , Gregory Dolganovc,e , Allan I. Basbauma,b a Department of Anatomy, University of California San Francisco, San Francisco, CA 94158, USA b Department of Physiology, University of California San Francisco, San Francisco, CA 94158, USA c Department of Medicine, University of California San Francisco, San Francisco, CA 94158, USA d BiPar Sciences Inc., Brisbane, CA 94005, USA e Department of Infectious Diseases, School of Medicine, Stanford University, Stanford, CA 94305, USA a r t i c l e i n f o Article history: Received 15 March 2008 Received in revised form 13 September 2008 Accepted 15 October 2008 Keywords: GSE siRNA Genetic screens Neurite outgrowth MTGR1 GFI1 a b s t r a c t Integrins are transmembrane receptors that promote neurite growth and guidance. To identify regulators of integrin-dependent neurite outgrowth, here we used two loss of function genetic screens in SH-SY5Y neuroblastoma cells. First, we screened a genome-wide retroviral library of genetic suppressor elements (GSEs). Among the many genes identified in the GSE screen, we isolated the hematopoetic transcriptional factor MTGR1 (myeloid translocation gene-related protein-1). Treatment of SH-SY5Y cells with MTGR1 siRNA enhanced neurite outgrowth and concurrently increased expression of GAP-43, a protein linked to neurite outgrowth. Second, we transduced SH-SY5Y with a genome-wide GFP-labeled lentiviral siRNA library, which expressed 40,000 independent siRNAs targeting 8500 human genes. From this screen we isolated GFI1 (growth factor independence-1), which, like MTGR1, is a member of the myeloid transloca- tion gene on 8q22 (MTG8)/ETO protein complex of nuclear repressor proteins. These results reveal novel contributions of MTGR1 and GFI1 to the regulation of neurite outgrowth and identify novel repressors of integrin-dependent neurite outgrowth. Published by Elsevier B.V. 1. Introduction Despite significant advances in our understanding of the molecules that contribute to axonal growth, progress in overcom- ing the failure of central nervous system axons to regenerate after injury has been disappointing. Among the many factors that con- tribute to the poor regeneration of injured CNS axons are: reduced intrinsic growth capacity of the injured neurons, inability to over- come inhibitory molecules in the region of the injury, and failure to respond to growth promoting molecules in the region of the injury. As axonal growth is not hindered during development, it is reasonable to hypothesize that these properties are present in the developing neuron, but are lost or suppressed in the adult (Spencer and Filbin, 2004; He and Koprivica, 2004; Neumann et al., 2002). Importantly, manipulations of the molecular machinery of the damaged neuron in the adult can enhance growth, indicating that these properties can be reinstated (Neumann and Woolf, 1999; Neumann et al., 2002; Filbin, 2003). Indeed, in recent years several ∗ Corresponding author at: BiPar Sciences, 1000 Marina Blvd., Suite 550, Brisbane, CA 94005, USA. Tel.: +1 650 635 6045; fax: +1 650 635 6057. E-mail address: vossovskaya@biparsciences.com (V.S. Ossovskaya). small molecules and proteins that either promote or inhibit the growth of neurites and axons have been identified. Among these are guidance and signaling molecules (e.g. cAMP), secreted growth promoting and inhibitory molecules, including neurotrophins, netrins, slits, ephrins, semaphorins and myelin-associated proteins (Tessier-Lavigne and Goodman, 2000, 1996; Schnorrer and Dickson, 2004; Nakamura et al., 1998; Arevalo and Chao, 2005; Neumann and Woolf, 1999; Neumann et al., 2002). With a view to providing a more extensive catalogue of the molecular contribution to various complex processes, attention has turned to genetic screens (Nijman et al., 2005; Paddison et al., 2004). For example, several comprehensive screening meth- ods to study regulators of neuronal function have been described in drosophila, C. elegans and zebrafish (Hivert et al., 2002; Hua et al., 2005; Runko and Kaprielian, 2004; Shao et al., 2005). Here we describe the results of two functional genetic screens for repressors of neurite outgrowth. We generated and screened a genome-wide retroviral GFP-genetic suppressor element (GSE) library and a large- scale lentiviral siRNA library targeting 8500 genes. The screen integrated a highly sensitive and comprehensive functional and array-based analysis. Most of the targets that we identified fell into five major categories: receptors, proteoglycans, kinases adap- tor proteins and transcription factors. In this paper we report on 0165-0270/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.jneumeth.2008.10.031
  2. 2. V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 323 the contribution of MTGR1 (myeloid translocation gene-related protein-1) and GFI1 (growth factor independence-1), both of which belong to the same ETO/MTG8 (myeloid translocation gene on 8q22) protein complex (Hock and Orkin, 2006; McGhee et al., 2003; Amann et al., 2005). This complex is a transcriptional repressor that regulates proliferation and differentiation of hematopoietic and stem cells. We demonstrated that the MTGR1 and GFI1 neg- atively regulate ␤1-integrin-dependent elongation of neurites in human SH-SY5Y neuroblastoma cells. 2. Materials and methods 2.1. Cell culture SH-SY5Y cells were obtained from the ATCC and maintained in DMEM/F12 media (50:50; Gibco) supplemented with 1% non- essential amino acids (Gibco), 15% heat-inactivated fetal calf serum, FCS (HyClone), 100 ␮g/mL penicillin and 100 ␮g/mL streptomycin at 37 ◦C, 5% CO2. To induce differentiation, cells were plated on laminin-coated plates (Becton Dickinson Labware) and treated with 0–50 ␮M trans-retinoic acid (RA) (Sigma) for 36–72 h. Phoenix cells were obtained from the ATCC with permission of G. Nolan (Stan- ford University). AmphoPackTM-293 packaging cells were obtained from Clontech. Phoenix and AmphoPackTM-293 were maintained in DMEM media (Gibco) supplemented with 10% heat-inactivated fetal calf serum (HyClone), 100 ␮g/mL penicillin and 100 ␮g/mL streptomycin at 37 ◦C, 5% CO2. 2.2. GSE library construction A self-inactivating retroviral vector, pQCGFPL was constructed using the pQCXIX vector (Clontech) in which the CMV-IRES cassette was replaced with a CMV-EGFP-linker-adaptor cassette. The CMV- EGFP cassette was derived from the pLEGFP-N1 retroviral vector (Clontech). This adapter was used to insert the GSE library (Fig. 1A and B). SH-SY5Y cells were treated with 25 ␮M trans-retinoic acid (Sigma) for 0, 12, 24, 48 or 72 h. After incubation with RA, the SH-SY5Y cells were collected and used to construct the library, as previously described (Gudkov et al., 1994; Ossovskaya et al., 1996), with modifications provided below. Briefly, total mRNA was iso- lated from 107 SH-SY5Y cells using the RNAqueous Kit (Ambion, Austin, TX) in six independent reactions, according to the manufac- turer’s instructions. mRNA quality was determined by microfluidic analysis (Agilent® 2100 bioanalyzer with Caliper’s RNA LabChip® Kit; Agilent). mRNA with a 28S–18S rRNA ratio of >1.2 was used for poly(A)+ RNA purification. Poly(A)+ RNA was purified from 2.0 mg of total mRNA with two rounds of oligo(dT) selection using the Poly(A)Purist Kit (Ambion, Austin, TX) according to the manu- facturer’s instructions. The isolated poly(A)+ RNA with A260–A280 values in the range of 1.85–2.1 was used for cDNA synthesis. The purity and integrity of the poly(A)+ RNA were verified by formalde- hyde 2.5% agarose gel electrophoresis and capillary electrophoresis using the Agilent® 2100 bioanalyzer. PolyA + RNA was used for cDNA synthesis with the SMART cDNA library protocol (Clontech) in six independent reactions, according to the manufacturer’s instructions, with some modifi- cations. Briefly, for each reaction 1.0 ␮g of PolyA + RNA was mixed with 1.0 ␮L 10 ␮M oligo(dT) (CDS primer), 1.0 ␮L 10 ␮M SMART II Oligonucleotide, and deionized water for a total volume of 5.0 ␮L. The mixture was heated at 72 ◦C for 2 min, cooled on ice for 2 min and spun for 30 s. The reaction was followed by the addi- tion of 2.0 ␮L 5 reaction buffer (250 mM Tris–HCl, pH 8.3, 30 mM MgCl2, and 375 mM KCl), 1.0 ␮L 20 mM DTT, 1.0 ␮L 10 mM dNTP Fig. 1. Schematic illustration of the functional screen. (A) pQCGFPL retroviral vector used as the transfer vector for library delivery into SH-SY5Y cells. LTR: long terminal repeat; CMV: CMV promoter; EGFP: enhanced green fluorescent protein; L: linker. The self-inactivating feature of the vector is provided by a deletion of the U3 region in the 3 LTR. (B) The vector pQCGFPL-GSE carries the GSE library and GFP for selection of infected cells. PCR primers used for rescue of integrated provirus are illustrated by arrows. (C) Schematic illustration of retroviral transduction of SH-SY5Y cells with pQCGFPL-GSE library and the functional genetic screening strategy for inhibitors of neurite outgrowth. (10 mM each dATP, dCTP, dGTP, and dTTP; Amersham Pharmacia, Piscataway, NJ, USA), 0.5 ␮L of 100 U/␮L Power Script Reverse Tran- criptase (Clontech) and 28 U/␮L Anti-RNase (Ambion, Austin, TX, USA). The samples were incubated at 42 ◦C for 1 h followed by inac- tivation of reverse transcriptase at 72 ◦C for 7 min. Next 40 ␮L TE buffer (10 mM Tris–HCl, pH 7.4, 1 mM EDTA) were added to each sample, which were subsequently stored at −20 ◦C. To determine the number of PCR cycles necessary for optimal amplification of cDNA, 1.0 ␮L from each first-strand cDNA reaction mixture was combined with 10 ␮L 10× advantage polymerase buffer (40 mM Tricine–KOH, pH 9.2, 15 mM KOAc, 3.5 mM Mg(OAc)2), 1.0 ␮L PCR primer (5 -AAGCAGTGGTAACAACGCAGAGT-3 ), 2.0 ␮L 10 mM dNTP mix (10 mM dATP, 10 mM dCTP, 10 mM dGTP, and 10 mM dTTP), and 1.0 ␮L Advantage cDNA Polymerase Mix (Clontech). Samples were amplified using the following program: 1 cycle at 95 ◦C for 1 min, then 15 cycles at 95 ◦C for 15 s, 65 ◦C for 30 s and 68 ◦C for 6 min. After 15 cycles, 15 ␮L of the reaction mixture were transferred to a new 0.5-mL tube and subjected to three additional cycles; the remaining 85 ␮L of the PCR mixture were kept on ice. After three additional cycles, 5.0 ␮L of reaction mixture was aliquoted for analysis by agarose gel, while the remaining 10 ␮L of the reac- tion mixture were subjected to another three cycles, for a total of 21 cycles. To determine the optimal number of cycles, 5.0 ␮L of each of the reaction mixture (i.e., 15-, 18-, and 21-cycle PCR products)
  3. 3. 324 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 were analyzed on a 1.5% agarose gel in Tris–acetate EDTA (TAE) buffer. Based on the results of the agarose gel analysis, samples were subjected to additional cycles. Products of cDNA reactions were purified using the NucleoTrap PCR Purification Kit (Clontech) and eluted in 50 ␮L TE buffer. One microgram of PolyA + RNA yields up to 10 ␮g of purified cDNA. The synthesized cDNA was ana- lyzed with 0.8%, 1.5% and 3% agarose gels in Tris–acetate EDTA buffer. The size of synthesized cDNA ranged from 0.5 to 6 kb with enrichment at 0.9–3 kb, which is typical for human brain RNA. Ten micrograms of synthesized cDNA was digested with RsaI endonu- clase, which recognizes the sequence 5 -GTAC-3 , creating cDNA fragments with an average size of 100–250 bp. Ten micrograms of synthesized cDNA in the second independent reaction was son- icated and fractionated by Quick Spin Columns (Sephadex G-25, Sephadex G-50; Roche). The 25–250 bp cDNA fragments from both reactions were purified by electrophoresis and gel filtration using Sephadex G-25, G-50 and G-100 (Sigma). cDNA fragments with an average size of 25–350 bp were ligated to attB1 and attB2 Gateway adapters, according to the Gateway protocol (Gibco, Invit- rogen) and cloned into the pQCGFPL vector with the GATEWAY Cloning System (Gibco, Invitrogen), according to the manufac- turer’s instructions. The GSE library was amplified in TOP10 E. coli cells on 50 independent 150-mm LB agar plates containing 0.1% glu- cose. The complexity of the GSE library was 9.6 × 107 independent E. coli clones. The library was purified with the QIAGEN Plasmid Maxi Kit (QIAGEN) according to the manufacturer’s instructions and used to transfect packaging cells (see Section 2.1). 2.2.1. Transduction of SH-SY5Y cells with the GSE library The viral supernatant carrying the library was produced as described previously (Martens et al., 2000; Ossovskaya et al., 1996) with some modifications. Briefly, AmphoPack packaging cells (Clontech) were plated at 5 × 107 cells per 150 mm plate (Corning) 12 h prior to the transfection. After a 12 h incubation, the media was changed and packaging cells were transfected with 30 ␮g retro- viral plasmid DNA per plate using the FuGENE 6 reagent (Roche Applied Science), according to the manufacturer’s instructions. A total of 5 × 108 AmphoPack packaging cells were transfected with the GSE library. At 12 h after transfection, the media was replaced with growth medium and cell cultures maintained at 37 ◦C in 5% CO2 for 12–24 h, then at 32 ◦C in 6% CO2 for 24–36 h. Sixty hours after transfection, the virus-containing supernatant was collected from transfected packaging cells, filtered through 0.45 ␮m filters (Nalgene), supplied with 5 ␮g/mL Polybrene (Sigma, St. Louis, MO) and overlayed on SH-SY5Y target cells for 12 h. (The SH-SY5Y cells had been plated for 12 h prior to retroviral transduction, with a seeding density of 2500 cells/cm2.) A total of 5 × 108 SH-SY5Y cells were transduced with the GSE library. The medium was changed 12 h after retroviral transduction and then again 12 h later. Ninety-six hours after retroviral transduction, the SH-SY5Y cells were detached with trypsin and plated on laminin- coated plates (Becton Dickinson Labware) with a seeding density of 2500 cells/cm2 and then treated with 25 ␮M trans-retinoic acid (Sigma) for 36 h to induce neurite outgrowth. Then the SH-SY5Y cells were collected with No-Zyme solution (Sigma) and immunos- tained with an anti-␤1 integrin antibody directly conjugated to APC (Pharmingen) as described below. The fluorescently labeled cells were subsequently analyzed by flow cytometry for GFP expression and ␤1 integrin immunostaining. 2.3. Screening the human siRNA library The human siRNA library contained about 43,000 siRNA tar- geting 8500 human genes and was cloned into an FIV-based pSIF1-H1 vector, as previously described (System Biosciences, SBI). The pSIF1-H1 vector carried GFP, which was used as a reporter for fluorescence-activated cell sorting (FACS) analysis (Fig. 8A). Briefly, the siRNA library targeted and overlapped with the 8500 genes represented on the GeneChip® Human Genome Focus Array (Affimetrix). The viral supernatant carrying the library was produced as described previously (Zufferey et al., 1998). Briefly, the siRNA library was transfected into 293T packaging cells (ATCC) with the pFIV-PACK packaging plasmid mix (Zufferey et al., 1998; System Biosciences; Cellecta, Mountain View, CA). The virus-containing supernatant was filtered through 0.45 ␮m filters (Nalgene), and stored at −70 ◦C. Prior to infection of cells with library-carrying virus, the viral supernatant was thawed for 5 min at 37 ◦C and immediately used to infect 5 × 108 SH-SY5Y cells. (The SH-SY5Y cells had been plated 12 h prior to viral transduction and were distributed in 16 six-well plates, with a seeding density of 2 × 106 cells/well.) Three hundred microliters of supernatant and polybrene, at 5.0 ␮g/mL (Sigma, St. Louis, MO) final concentra- tion, were added to each well. The plates were spun in a swinging bucket rotor centrifuge for 90 min at 1500 × g and 25 ◦C and then incubated at 32 ◦C, 5% CO2. Ninety-six hours after viral trans- duction, the SH-SY5Y cells were detached with trypsin, plated on laminin-coated plates (Becton Dickinson Labware) at a seed- ing density of 2500 cells/cm2 and then treated with 25 ␮M RA (Sigma) for 36 h to induce neurite outgrowth. Then the SH-SY5Y cells were collected with No-Zyme solution (Sigma) and immunos- tained with an anti-␤1 integrin antibody directly conjugated to APC (Pharmingen) as described below, for subsequent analysis by flow cytometry for GFP expression and ␤1 integrin immunostain- ing. 2.4. Flow cytometric (FACS) analysis and sorting To detect ␤1 integrin in live cells, the cells were detached from the plates with No-Zyme solution (Sigma), washed twice with cold PBS and then stained with an anti-␤1 integrin antibody directly conjugated to APC (Pharmigen). Briefly, 5.0 ␮L of APC-coupled anti- ␤1 integrin antibody, which recognizes an extracellular epitope of ␤1 integrin, was used to stain 1 × 106 cells. The SH-SY5Y cells were incubated with antibody in growth media, with 3% heat-inactivated FCS (HyClone), for 1 h on ice. Next the cells were washed three times with PBS, resuspended in cold PBS, analyzed and sorted by FACS (BD Biosciences). Data were analyzed with FACScan, CellQuest and FlowJo software. 2.5. Rescue of integrated provirus and identification of target genes: GSE library From each sorted population, 1–5 × 106 cells were spun and resuspended in PBS to purify genomic DNA, accord- ing to the manufacturer’s instructions (DNeasy Tissue Kit, Qiagen). Isolated genomic DNA was used as a template for PCR reactions. To isolate the integrated provirus from genomic DNA we used PCR with forward primers designed to EGFP: 5 -ACTCTCAAGGATCTTACCGCTGTTGAGATC-3 and 5 - AGATCCAGTTCGATGTAAC CCACTCGTGCA-3 and a reverse primer (5 -ACCCAGCTTTCTTGTACAAAGTGGT AGGTAGGTAGG-3 ), which corresponds to the Gateway cloning adapter (GATEWAY Cloning System, Gibco, Invitrogen). After the PCR reaction, the PCR products were analyzed with 1.5% and 3% agarose gels in TAE buffer. Relevant PCR fragments were cloned into the pCR2 vector (Invitrogen) and then individual clones were isolated with the QIAGEN Plasmid Mini Kit (QIAGEN) and then sequenced to identify GSE library clones.
  4. 4. V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 325 2.5.1. Rescue of integrated provirus and identification of the target genes: siRNA library Total mRNA and genomic DNA from sorted cells were isolated with the Trizol reagent (Invitrogen) according to the manufacturer’s instructions. Reverse transcription was performed according the GeneNet protocol (System Biosciences). The isolation of integrated provirus carrying siRNA was performed by PCR using TitaniumTM Taq DNA polymerase (Clontech) as previously described (System Biosciences, Cellecta, Mountain View). Next PCR products were re-amplified with biotinylated primers, purified with QIAGEN’s QIAquick PCR Purification Kit (QIAGEN) and then analyzed with the Affymetrix GeneChip® Human Genome Focus Array accord- ing to the manufacturer’s instructions and with recommended Affymetrix reagents at 45 ◦C, without DMSO for 16 h (Affymetrix, System Biosciences, Cellecta, Mountain View, CA). Data analysis was performed with GeneNet software (System Biosciences, Cel- lecta, Mountain View, CA) and Pathway Studio (Ariadne Genomics, Rockville, MD). 2.6. siRNA transfection SH-SY5Y cells were plated on 24-well laminin-coated plates (Becton Dickinson) at a seeding density of 2500 cells/cm2 12 h before transfection. The siRNA was synthesized commercially (Dharmacon, Thermo Fisher Scientific). The following siRNA was used as a control: 5 -UAGCGACUAAACACAUCAAUU-3 . Transfec- tions with siRNA were performed as previously described (Reynolds et al., 2004). Forty-eight hours after transfection the cells were treated with either 0 or 25 ␮M RA. Neurite outgrowth was quan- tified at 36, 48 and 72 h after the RA treatment. To this end, the cells were fixed and stained with DAPI (Molecular probes) and phalloidin-TRITC (Sigma) as previously described (Gallo and Letourneau, 1998). Neurite length was measured as described below. 2.7. Gene transcriptional profiling by quantitative real-time RT-PCR Total mRNA was isolated from FACS-sorted cells with the RNeasy Kit as recommended by the manufacturer (Qiagen). Isolated mRNA was analyzed with the Agilent 2100 bioanalyzer and the RNA 6000 Nano LabChip Kit (Agilent Technologies, Palo Alto, CA). Next, 20–40 ng of total mRNA was reverse transcribed into cDNA using PowerScript (BD Biosciences, Palo Alto, CA) with random hexamers. The resultant cDNA (30 ␮L) was amplified by PCR with a mixture of gene-specific primers (5.0 pM each) and Advantage 2 TaqDNA poly- merase (BD Biosciences) to produce amplicons <250 bp. For details see Dolganov et al. (2001). Next the amplified cDNA was used to quantify individual gene expression via quantitative real-time RT-PCR, using TaqMan probes and primers, and with Universal Master Mix (Invitrogen) as described previously (Woodruff et al., 2007). All assays were performed with an ABI Prism 7900 Sequence Detection Systems (Applied Biosystems). The real-time RT-PCR Ct values were con- verted to relative gene copy numbers (Dolganov et al., 2001). For controls and to normalize results we monitored expression of the following genes: GAPDH, PPIA, EEF1A, RPL13A, UBIQUITIN B and TBP. Gene-specific primers for real-time RT-PCR were designed for each gene of interest using either Primer3 (http://jura.wi. mit.edu/rozen/) or Primer Express software (PerkinElmer) based on sequencing data from the NCBI database. Primers were purchased from Biosearch Technologies (Novato, CA). For primer sequences, see http://adgenomics.stanford.edu. Normalized gene expression data were analyzed by t-test, so as to identify genes with statisti- cally significant changes in expression. 2.8. cDNA cloning Human spinal cord mRNA was obtained from Clontech (Clon- tech). The quality of the mRNA was analyzed by capillary electrophoresis using the Agilent 2100 bioanalyzer and RNA 6000 Nano LabChip Kit (Agilent Technologies, Palo Alto, CA). cDNA syn- thesis was performed with the SMART PCR cDNA Synthesis Kit and Advantage 2 PCR Kit (BD Biosciences) according to the manufac- turer’s instructions. cDNA was used for MTGR1 cDNA amplification by PCR with specific primers designed for the 5 and 3 ends of MGTR1 and the Advantage 2 PCR Kit (BD Biosciences). Isolated cDNA was re-amplified with primers containing Gateway attB adapters, according to the Gateway protocol (Gibco, Invitrogen) and cloned into the pQCGFPL vector with the GATEWAY Cloning System (Gibco, Invitrogen) according to the manufacturer’s instructions. The accuracy of the GFP-MTGR1 fusion and absence of mutations in the pQCGFPL-MTGR1 construct were confirmed by sequencing and BLAST analysis (NCBI). 2.9. Immunofluorescent analyses and image capture EGFP expression in live cells was monitored and analyzed with a Nikon TE200 inverted microscope equipped with an Orca ER cooled charge-coupled device (CCD) camera (Hamamatsu, Middlesex, NJ). Images were collected and analyzed with the Simple PCI software (Compix). To analyze neurite outgrowth, the SH-SY5Y cells were plated at a seeding density of 1000 cells/cm2 in growth media and incubated 12–18 h at 37 ◦C, 5% CO2. To label neurites, cells were fixed with 4% paraformaldehyde in 100 mM PBS, pH 7.4 (15 min at 4 ◦C), and washed for 15 min with 1× PBS containing 0.1% saponin and 1% FCS. The fixed cells were washed with 3× PBS, permeabilized (0.1% Triton X-100-PBS, pH 7.4; 15 min) and stained with 50 ␮g/mL phalloidin-TRITC in PBS containing 1% FCS for 2 h at 4 ◦C. Next the cells were washed with 3× PBS followed by incubation with 10 ␮g/mL 4 -6-diamidino-2-phenylindole, DAPI (Molecular Probes) for 10 min, washed in 3× PBS and analyzed with a Nikon TE200 microscope (Nikon) or with the Cellomics ArrayScan VTI (Thermo Fisher Scientific). 2.9.1. Manual image capture and analysis The images of cells plated on six-well plates (Corning) were captured manually with an inverted Nikon TE200 microscope and an ORCA-ER CCD digital camera. From the digitized images we measured neurite/process length with the Simple PCI software (Compix). Briefly, we first manually captured random rectangu- lar fields of cells. Next, the corners of the rectangular fields were connected with digital diagonal lines, so as to define an unbiased determination of the cells to be measured. We then measured the length of neurites/processes from 100 cells whose processes crossed the diagonals. 2.9.2. Automated image capture and analysis For automated analysis, we captured the images of cells plated on 96-well plates with a Celllomics ArrayScan® VTI, which includes a high-resolution Zeiss optical system, a multiple bandpass emis- sion filter with matched single band excitation filters (XF57 or XF100, Omega Optical), CCD camera and an ArrayScan VTI HCS Reader (Thermo Scientific Cellomics). The system provides simul- taneous image capture of several fluorophores in the same cell. The Cellomics algorithm identifies cells, evaluates the integrity of a nucleus and automatically excludes dead cells from the analysis. Dual emission images were acquired from six discrete fields in each
  5. 5. 326 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 Fig. 2. Retroviral transduction and delivery of pQCGFPL-GSE library into SH-SY5Y cells. (A and B) The magnitude of the delivery of GSE library into packaging cells was estimated by fluorescence microscopic analysis of GFP expression (A) and by FACS (B). A total of 2 × 104 cells were analyzed by FACS. The oval (B) identifies the viable cells (90%—control cells and 90.4%—transfected cells). The histograms (B) illustrate GFP-negative and GFP-positive cells. The upper histogram represents control cells; lower histogram represents packaging cells transfected with GSE library (99.2%). (C and D) The magnitude of the delivery of GSE library into target SH-SY5Y cells by retroviral transduction was estimated by fluorescence microscopic analysis of GFP expression (C) and by FACS (D). The oval gate (D) identifies the viable cells (71.7%—control cells and 72%—transduced cells). The histograms (D) illustrate GFP-negative and GFP-positive SH-SY5Y cells. The upper histogram represents control cells; lower histogram represents SH-SY5Y cells transduced with the GSE library (50.3%). FSC-H: forward scatter; SSC-H: side scatter. (E) Image of SH-SY5Y cells transduced with vector control (without library). (F–K) SH-SY5Y cells transduced with GSE library (scale bar equals 50 ␮m). (L) Red: SH-SY5Y cells transduced with control GFP vector. Blue: SH-SY5Y cells transduced with GSE-GFP library. All cells were labeled with ␤1 integrin antibody directly conjugated to allophycocyanin (APC; see Section 2). Rectangle defines the sorted population
  6. 6. V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 327 well of the plate. From these images, neurite measurements were calculated with an automated quantitative high content screen- ing algorithm for neurite outgrowth (Thermo Scientific Cellomics) according to the manufacturer’s instructions. 2.9.3. Bioinformatics Bioinformatic analysis was performed using the NCBI and EMBL databases. To gain insight into particular biological pathways we used the PathwayStudio and PathwayExpert software packages (Ariadne Genomics, Rockville, MD). 3. Results Fig. 1C illustrates the strategy that we followed in the first screen. We generated a library from SH-SY5Y cells, which is a sympathetic nervous system-derived, clonal human neuroblastoma cell line. We chose SH-SY5Y cells for several reasons. SH-SY5Y cells exhibit a dis- tinct neuronal phenotype when grown on laminin and treated with neurotrophic factors or retinoic acid (Pahlman et al., 1981; Kaplan et al., 1993; Jalava et al., 1992, 1993; Jamsa et al., 2004). Differentiated SH-SY5Y cells express a variety of neuronal markers, including GAP- 43, synaptotagmin-l and synaptobrevin (Kim et al., 2000; Goodall et al., 1997). Most importantly, retinoic acid-stimulates the devel- opment of neurite-like processes (Clagett-Dame et al., 2006), and their growth can be readily monitored using surface marker expres- sion. This greatly facilitates high throughput screening for genes that regulate neurite outgrowth. Given these properties, SH-SY5Y cells, although not equivalent to primary neurons, provide an excel- lent surrogate with which to perform and validate the utility of this high throughput screen. 3.1. Library construction Fig. 1A and B illustrate the design of the retroviral vector that we used for the library construction. We created and used a self- inactivating retroviral vector because after retroviral integration of the vector into the target cell genome, transcription is driven only from the CMV promoter. This property is achieved through dele- tion of the U3 region of the 3 LTR in the pQCGFPL vector, and is an important feature that significantly increases the fidelity of the system (Fig. 1A and B). The library covered the entire SH-SY5Y genome and included combinatorial random fragments of cDNA (25–350 nucleotides long) in both the sense and antisense orientations. We designed 5 and 3 adapters for directional cloning and the 3 adapter included stop codons in all three frames. Members of the library function as either antisense mRNA molecules (if they were cloned in an anti- sense orientation) or lead to the production of functional interfering peptides (if the inserts were cloned in the sense orientation). An important feature of the library is that each random library clone is fused to GFP; this greatly facilitates subsequent recovery of func- tional inserts during the FACS protocol (see below). The genetic complexity of the library was 1 × 107 independent recombinant clones. 3.2. Retroviral transduction We chose a retroviral strategy for the following reasons. First, retroviruses permit efficient and accurate delivery of genes and recombinant clones into target cells. Second, there is stable inte- gration of the provirus into target genome, resulting in long-term expression of the encoded genetic information, including the GFP. Third, under optimal conditions, only one virion can be incorpo- rated per cell (Kustikova et al., 2003; Miller, 2002). This greatly facilitates high throughput recovery and subsequent identification of functional library clones, namely those that alter process out- growth in SH-SY5Y cells. We found that >99% of the packaging cells were transfected with the library. This was established by visualization of GFP flu- orescence (Fig. 2A) and by FACS analysis (Fig. 2B). We obtained about 55% efficiency in the delivery of the library into SH-SY5Y cells (Fig. 2C and D) and we also observed a significant functional impact of the library. Thus, in cells infected with the control vector, pQCGFPL (Fig. 2E), the GFP was uniformly distributed. By con- trast, after transduction of the SH-SY5Y cells with the library, we observed significant phenotypic variability of the GFP expression (Fig. 2F–K). In some cases, the GFP was exclusively cytoplasmic and in the processes of the cells; in other examples the GFP had clearly translocated to the nucleus (Fig. 2H). We also observed structural changes, including cells with elongated processes (Jamsa et al., 2004). 3.3. High throughput functional screening for increased process/neurite outgrowth The next step was to identify a marker that we could use to screen for genes that influence process length. Ideal markers for screening should either be upregulated in the population of cells with growing processes or should exclusively be expressed on developing growth cones of cells that are differentiating. In a preliminary analysis we monitored the expression of a variety of markers of differentiated SH-SY5Y cells using real-time PCR. Based on this analysis we turned our attention to the integrins, which are the family of receptors through which extracellular matrix molecules, including laminin, interact. Of particular importance to the present approach is that there is increased ␤1 integrin expres- sion on the growing neurites of embryonic retinal ganglion cells (Neugebauer and Reichardt, 1991; Stone and Sakaguchi, 1996; Ivins et al., 1998; Treubert and Brummendorf, 1998). ␤1 integrin is also localized on the axons and growth cones of regenerating facial nerve and the magnitude of ␤1 integrin immunoreactivity corre- lates with axonal growth of dorsal root ganglia neurites (Arevalo and Chao, 2005). As illustrated in Fig. 3A–E, retinoic acid dose-dependently induced a neuronal phenotype in SH-SY5Y cells and, consistent with a previous report (Jenab and Inturrisi, 2002; Li et al., 2000), we found that RA significantly upregulated ␤1 integrin mRNA in cells grown on laminin (Fig. 3F). Furthermore, using FACS analysis to follow these changes in a high throughput fashion we found that the level of ␤1 integrin protein in RA-treated SH-SY5Y cells was also significantly increased (Fig. 3G). For these studies we used an antibody that recognizes an extracellular epitope of ␤1 integrin in live non-permeabilized cells. These results demonstrate that RA induction of processes in SH-SY5Y cells occurs concomitantly with increased ␤1 integrin expression on the surface of the cells and on their processes. Finally, consistent with the conclusion that this increase indeed represents neurite outgrowth, we also found an increase of the cytoplasmic marker, GAP-43, a protein associated with neurite growth (Bomze et al., 2001; Anderson et al., 1998; Skene, 1989; Fig. 3G). of viable, GFP-positive cells that over-express ␤1 integrin, after transduction with the GSE library. This population corresponds to 1.42% of the GFP and ␤1 integrin—APC- positive cells that were isolated by FACS (the fluorescence intensity is a logarithmic readout of the magnitude of antibody binding). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
  7. 7. 328 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 Fig. 3. Retinoic acid (RA) induces neurite outgrowth in SH-SY5Y cells. (A–E) SH-SY5Y cells differentiated and extended neurites after treatment with RA. SH-SY5Y cells were plated onto laminin and 24 h after plating, the cells were treated with 0, 12.5, 25 or 50 ␮M RA for 72 h (scale bar equals 50 ␮m). Images were captured with an ORCA-ER CCD digital camera and neurite length (see Section 2). (E) Average length of neurites of SH-SY5Y cells plated on laminin without RA or treated with 50 ␮M RA. (F) Quantitative real-time RT-PCR-based transcriptional profiling of SH-SY5Y cells after treatment with RA (see Section 2). Results are means ± S.E.M. of pooled data from three separate experiments (for abbreviations and the panel of genes analyzed see Supplementary Table 1). (G) FACS analysis of the immunostaining of live SH-SY5Y cells plated on laminin, treated with 0 or 25 ␮M RA for 72 h and stained with ␤1 integrin antibody. Cells were labeled with ␤1 integrin antibody directly conjugated to allophycocyanin (APC) or isotype control antibody directly conjugated to APC. Data were collected by FACScan and analyzed with CellQuest and FlowJo softwares. To confirm that the magnitude of ␤1 integrin indeed correlates with the extent of neurite outgrowth we next monitored neu- rite outgrowth after downregulation of ␤1 integrin with siRNA. Twenty-four hours after transfection of the ␤1 integrin siRNA, we treated the cells with RA. ␤1 integrin siRNA significantly reduced ␤1 integrin mRNA by about 50% (Fig. 4A), and most importantly, this reduction correlated with a dramatic inhibition of neurite out- growth (Fig. 4B) compared to the growth observed in the absence of ␤1 integrin siRNA or in cells treated with control luciferase siRNA (Fig. 4B). Having established that increases in surface ␤1 integrin immunoreactivity correlate with the RA-induced neuronal phe- notype, we next developed a FACS protocol to rapidly sort cells transduced with the library. Three sorting gates were used: (1) GFP, which marks cells that were transduced with the library; (2) APC, which marks cells immunostained with an APC-coupled anti-␤1 integrin antibody; (3) cell size, so as to exclude dead cells. The control groups consisted of cells immunostained with iso- type control antibody conjugated to APC or of cells transduced with empty vector-GFP and immunostained with APC-coupled ␤1 integrin antibodies. From 108 library-transduced SH-SY5Y cells we sorted the top 1.42% of ␤1 integrin expressors that were also GFP- positive, were viable and did not overlap with the population of cells infected with the control, empty-GFP vector (Fig. 2L). 3.4. Rescue of functional clones Next we used specific primers to isolate the provirus from sorted cells. The primers were designed to the backbone of the vector or to GFP (Fig. 1B). All PCR products were individually sequenced to identify candidate genes. We found that 80% of the isolated clones had inserts in the antisense orientation; 20% of isolated clones were sense-oriented and in frame with GFP. In our first analysis, which established the utility of this approach, we analyzed 30 clones and recovered a variety of genes, including transcription factors, scaffold proteins, channels and several genes for which a function has yet to be identified. Below we describe some of these genes and demonstrate their functional relevance to process/neurite out- growth.
  8. 8. V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 329 Fig. 4. Transfection of SH-SY5Y cells with ␤1 integrin siRNA. SH-SY5Y cells transfected with ␤1 integrin siRNA or a control siRNA. (A) Quantitative real-time RT-PCR-based transcriptional profiling of SH-SY5Y cells 48 h after transfection with ␤1 integrin siRNA. Data, expressed as relative gene copy number, were normalized to the mRNA level in cells transfected with control siRNA and were determined from the average of three independent experiments (see Supplementary Table 1). (B) Digital images of SH-SY5Y cells transfected with ␤1 integrin siRNA or control siRNA and treated with 25 ␮M RA for 60 h (scale bar equals 50 ␮m). 3.5. siRNA inhibition of MTGR1 expression induces neurite outgrowth Our attention was directed at a clone that encodes the MTGR1 gene, which we chose to further characterize, for three reasons. First, we isolated three independent, although not identical, clones corresponding to the MTGR1 gene. Second, MTGR1 is a transcrip- tional co-repressor that has been implicated in the regulation of differentiation of hematopoetic cells (Kitabayashi et al., 1998; Calabi and Cilli, 1998; Rossetti et al., 2004). Third, MTGR1 is a human homologue of XETOR, which inhibits neurogenesis in Xenopus (Cao et al., 2002). Because the MTGR1 clones were isolated from the library in the antisense orientation we concluded that the increase of ␤1 inte- grin expression resulted from inhibition of MTGR1. To confirm this observation and to test the hypothesis that increased ␤1 integrin expression is associated with increased process/neurite outgrowth, we developed a secondary screen using siRNA to reduce MTGR1 mRNA expression in an in vitro analysis of neurite outgrowth in SH-SY5Y cells. Fig. 5A demonstrates that transfection of SH-SY5Y cells with MTGR1 siRNAs reduced MTGR1 mRNA by greater than 80%. Consis- tent with the FACS analysis we also found that inhibition of MTGR1 by siRNA concurrently induced a pronounced increase of ␤1 inte- grin and GAP-43 mRNA (Fig. 5E). Finally, we found an increase of epidermal growth factor (EGF) and fibroblast growth factor-1 (FGF- 1) mRNA, both of which induce neurite outgrowth in SH-SY5Y cells (Kornblum et al., 1990; Morrison et al., 1988), even in the absence of RA (Fig. 5C and D). Importantly, we found no changes in the expres- sion of a panel of other genes that were analyzed in SH-SY5Y cells (Supplementary Table 1 and Section 2). We also performed several additional controls. First, we tar- geted SH-SY5Y cells with an siRNA designed to another clone that was isolated during the primary screen, Sox4. Despite significant knockdown of Sox4 mRNA with Sox4 siRNA (Fig. 5A), we found no change in the level of ␤1 integrin, GAP-43, EGF or FGF-1 mRNA (data not shown). Finally, we used an siRNA directed against luciferase and also found no changes in mRNA levels of these genes (Fig. 5C and D). Most importantly, morphological analysis of SH-SY5Y cells treated with MTGR1 siRNA demonstrated an increase in process Fig. 5. Effect of MTGR1 siRNA on SH-SY5Y cells. Quantitative real-time PCR-based transcriptional profiling of MTGR1 and Sox4 (A), FGF-1 (C), EFG (D), ␤1 integrin and GAP-43 (E) 48 h after transfection with MTGR1, Sox4 or luciferase siRNA in SH-SY5Y cells. Values are normalized to mock transfected cells. (B) Quantification of neurite outgrowth from SH-SY5Y cells plated onto laminin and treated with 0 or 25 ␮M RA for 72 h.
  9. 9. 330 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 Fig. 6. Overexpression of full length MTGR1 cDNA in SH-SY5Y cells. (A, top) SH- SY5Y cells transduced with control pQCGFPL vector. (B) SH-SY5Y cells transduced with retroviral vector pQCGFPL-MTGR1 containing MTGR1 full length cDNA isolated from human spinal cord and fused to GFP. In (B, bottom), there is nuclear localization of the MTGR1. length (Fig. 5B). This was the case both for SH-SY5Y cells incubated without RA (43% increase) or in the presence of RA (40% increase). In fact, the length of processes in the presence of MTGR1 siRNA, but without RA, was comparable to the length of processes in con- trol, RA-treated cells. We found no effect of control siRNAs or Sox4 siRNA on process length (Fig. 5B). 3.6. Overexpression of MTGR1 inhibits neurite outgrowth We next studied the effect of full length MTGR1 cDNA overex- pression in SH-SY5Y cells. We isolated full length MTGR1cDNA from adult human spinal cord by RT-PCR and cloned it as a GFP-fusion into the retroviral vector pQCGFPL (Fig. 1A) to generate pQCGFP- MTGR1. We then infected a naïve population of SH-SY5Y cells with pQCGFP-MTGR1 and 72 h later isolated the GFP-positive cells by FACS. In cells transduced with a control, empty GFP vector, there is uniform distribution of the GFP fluorescence (Fig. 6A). By contrast, Fig. 6B illustrates that the GFP-MTGR1 protein is only found in the nucleus of infected SH-SY5Y cells, which is consistent with the reports that MTGR1 is member of the ETO family of nuclear repres- sor proteins (Davis et al., 2003; Lindberg et al., 2003). When we studied the consequence of overexpression of GFP-MTGR1 in RA- stimulated SH-SY5Y cells, we observed a significant inhibition of process outgrowth (Fig. 7E–H) compared to cells transduced with empty GFP vector (Fig. 7A–D). These results provide evidence that human MTGR1 is indeed a repressor of process/neurite outgrowth in the SH-SY5Y cells. Fig. 7. Overexpression of MTGR1 cDNA inhibits neurite outgrowth in SH-SY5Y cells. SH-SY5Y cells transduced with GFP-vector control (A–D) or with GFP-MTGR1 (E–H) and treated with or without RA for 72 h. (A and E) Phalloidin-TRITC staining; (B and F) GFP; (C and G) overlay of phalloidin-TRITC and GFP; (D and H) overlay of phalloidin-TRITC and DAPI (scale bar equals 50 ␮m). 3.7. Expression profile of MTGR1 Although Morohoshi et al. (2000) demonstrated that MTGR1 is expressed at high levels in human bone marrow cells, hemopoi- etic tissues and lymphoid organs, its presence in human neuronal tissue was not reported. For this reason, in the present study we analyzed expression of MTGR1 in human primary neuronal tissues. We found high levels of MTGR1 mRNA in cerebellum, spinal cord, motor cortex, hippocampus and substantia nigra (Supplementary Fig. 1). These results suggest that our observations in SH-SY5Y cells are of general relevance to neurite outgrowth in diverse regions of the central nervous system. 3.8. Parallel and independent screening using a lentiviral siRNA library In a parallel and independent functional genetic screen we used a GFP-labeled large-scale siRNA library that is expressed from an FIV-based lentiviral vector pSIF1-H1 (Fig. 8A). The library contains 40,000 individual siRNA that target 8500 human genes (Section 2). We delivered this library to naive SH-SY5Y cells and, as in the previous screen, we found that the library-induced diverse mor- phological changes of the SH-SY5Y cells (Fig. 8C) compared to cells expressing vector control (Fig. 8B).
  10. 10. V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 331 Fig. 8. Lentiviral-based siRNA screening. (A) pSIF-H1 lentiviral vector used for siRNA library expression and delivery. LTR: long terminal repeat; gag: structural protein; RRE: rev responsive element; cPPT: central polypurine tract; CMV: CMV promoter; GFP: green fluorescent protein; WPRE: woodchuck hepatitis virus posttranscriptional regulatory element. The siRNA library under the control of the H1 promoter was inserted into the 3 UTR of the pSIF lentiviral vector. (B) SH-SY5Y cells transduced with empty pSIF-GFP control vector. (C) Neurite outgrowth induced in SH-SY5Y cell transduced with pSIF-siRNA library (scale bar equals 50 ␮m). We performed two independent screens of the lentiviral siRNA library. To identify functional siRNA, we used a modification of the FACS approach described above and isolated an experimental and a control population of cells. The experimental population showed high upregulation of extracellular ␤1 integrin. The control popu- lation showed levels of ␤1 integrin expression comparable to that recorded in either vector-GFP transduced cells or in untreated cells. To identify functional clones we isolated integrated provirus from both populations of sorted cells, using primers designed to the vector (Section 2). Instead of sequencing individual clones from the sorted cells, we used a high throughput approach (Affymetrix GeneChip® Human Genome Focus Array, Section 2) to identify siR- NAs that induced the increased ␤1 integrin expression. From the analysis of two independent screens of the lentivi- ral siRNA library we identified 39 genes that appeared in both screens. Among these genes were many that encode proteogly- cans, kinases, receptors, adaptor proteins and transcription factors (Supplementary Table 2). In several instances, the same gene was uncovered by several independent siRNAs, among which are those corresponding to protein tyrosine kinase HEK2, Rab GDI, neuro- oncological ventral antigen 1 (NOVA1) and neurite outgrowth inhibitor (NOGO). Of particular interest and relevance to the results obtained in the GSE screen, is that from the lentiviral screen we isolated an siRNA that targets GFI1 and GF1B. A bioinformatic analysis (Path- way Studio, Ariadne Genomics) revealed that GFI1 is a zinc finger transcriptional repressor and a member of the MTG8(ETO)/MTGR1 protein complex (Hock and Orkin, 2006; McGhee et al., 2003). GFI1B is 97% identical in the zinc finger domain and 95% identi- cal in the SNAG domain to GFI1 (Garcon et al., 2005). Thus, using two independent genome-wide functional genetic screens of genes that regulate neurite outgrowth we uncovered the same protein complex. 4. Discussion In this paper we describe a powerful, high throughput genetic strategy that uses a functional genetic screen of two large- scale libraries in human neuronal cells. The success of this screening approach is illustrated by our discovery that the MTG8(ETO)/MTGR1 protein complex is a significant contributor to the molecular mechanisms that underlie and regulate ␤1 integrin- dependent neurite elongation/outgrowth in SH-SY5Y cells. These novel primary and secondary screens establish a powerful system for comprehensive identification of genes that regulate neurite out- growth. A great advantage of the GSE library is that it is genome wide and is syngenic for the targeted SH-SY5T cells. To complement this we used a large-scale siRNA library that targeted 8500 human genes. In both screens we delivered libraries by retroviral transduction into SH-SY5Y cells. We used retroviral vectors, as under optimized conditions the cells only take up a single virion during retrovi- ral transduction (Kustikova et al., 2003). This made it possible to follow the long-term phenotypic changes produced by individual elements of the library, in individual cells, which also greatly sim- plified subsequent identification of functional clones. We were searching for a relatively rare event. Thus, it was essential to have a high throughput method of analysis. To this end, we used multiparameter fluorescence-activated cell sort- ing to identify phenotypic changes induced by the libraries and to select cells that significantly manifest these changes. There are several features of our approach that greatly facilitated FACS
  11. 11. 332 V.S. Ossovskaya et al. / Journal of Neuroscience Methods 177 (2009) 322–333 analysis. Most importantly, we identified an antigen that could be used to monitor library-induced phenotypic changes, namely ␤1 integrin. It is theoretically possible to monitor intracellular mark- ers, but this generally requires fixation and permeabilization of cells, which complicates the subsequent identification of functional clones. Moreover, we found that monitoring intracellular markers of neurite outgrowth in fixed cells significantly reduced the effi- ciency of the functional screen as it was more difficult to recover the functional provirus or clones of interest from the library. Because the library carried GFP, the sorting could be restricted to cells that incorporated the library. Taken together, we were able to screen rapidly millions of cells and to identify functional clones. The approach can be readily adapted to other cell types and phenotypic changes. To confirm the functional relevance of candidate clones iso- lated in the primary GSE screen, we used a secondary, cell-based assay of neurite outgrowth. This functional screen involved siRNA suppression of the expression of candidate genes isolated in the primary screen. Importantly, we not only monitored the same end- point as in the primary screen (namely ␤1 integrin expression) but we also documented that upregulation of ␤1 integrin cor- related with neurite outgrowth. In a further confirmation of the functional significance of the candidate genes, we overexpressed full-length cDNA corresponding to the candidate gene, and as pre- dicted, observed inhibition of neurite outgrowth. 4.1. Leniviral siRNA screen and high throughput analysis To confirm the results from the GSE screen we performed two additional independent functional screens of a large-scale lentivi- ral library of 40,000 siRNAs that target 8500 characterized human genes. From FACS analysis of cells sorted for ␤1 integrin upregu- lation, we identified positive clones using a microarray approach. Among isolated genes we found chondroitin-sulfate proteoglycan (Versican), which has been previously described as an inhibitor of CNS axon growth in vitro and in vivo (Schweigreiter et al., 2004). Of particular interest, however, was our isolation of GFI1 and GFIB, members of the MTGR1 protein complex (see below). This discov- ery provided a very strong validation, not only of the utility of the primary screen to identify genes that influence neurite outgrowth, but also of the importance and value of the secondary, independent screen. The two screening approaches clearly complemented each other. 4.2. MTGR1, GFI1 and neurite outgrowth From these two independent genome-wide functional screens we isolated myeloid-transforming gene-related protein 1 and growth factor independence 1 and 1B. MTGR1 heterodimerizes with the myeloid translocation gene on chromosome 8, MTG8, also known as eight-twenty-one or ETO (Kitabayashi et al., 1998). Gfi1 is a DNA binding transcriptional repressor that associates with MTG8/ETO and MTGR1 (McGhee et al., 2003; Amann et al., 2005). Thus, MTGR-1 and GFI1 belong to the same MTG8/ETO protein com- plex and represent a family of nuclear repressor proteins (Hock and Orkin, 2006; McGhee et al., 2003; Amann et al., 2005). Studies of homologs of MTGR1 have provided evidence consistent with our finding that inhibition of MTGR-1 promotes neurite outgrowth. For example, and as noted above, Xenopus XETOR, a homolog of human MTGR-1, is a transcriptional repressor that suppresses neurogene- sis during embryogenesis (Cao et al., 2002; Logan et al., 2005). The MTG8/ETO protein complex also blocks activity of G-CSF, which in human neural stem cells stimulates neurogenesis through recip- rocal interaction with VEGF and STAT activation (Ahn et al., 1998; Jung et al., 2006). Taken together with our finding that inhibition of MTGR1 also induces transcription of EGF and FGF-1, both of which contribute to the regulation of neurite outgrowth and axon guid- ance (Kornblum et al., 1990; Morrison et al., 1988; Arevalo and Chao, 2005), these results indicate that the MTGR1/ETO/GFI1 pro- tein complex function as nuclear repressors of pathways that can regulate neurite outgrowth. Acknowledgments We thank Alex Chenchik and Michail Makhanov (System Bio- sciences) for help with the lentiviral siRNA libraries, Valerie Vincent for help with the Cellomics analysis and Ariadne Genomics for help with bioinformatics. This work was supported by NIH grants NS14627 and 48499 and an Opportunity Award from the Sandler Program in Basic Sciences at UCSF. Appendix A. 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