This study is funded in part by a Department of Biotechnology grant BT/PR8742/AGR/36/773/2013 to SRV; and Department of Biotechnology India BT/COE/34/SP13432/2015 and Department of Science and Technology, India: EMR/2017/003880 to P.B. RVS and P.B. are supported by Wellcome DBT India Alliance IA/S/17/1/503118 and IA/S/15/1/501842, respectively. S.D. is supported by the CSIR-UGC fellowship, and S.I. is supported by an ICMR senior research fellowship. We thank Abhirup Bagchi, Sandya Rani, and the CSCR Flow Cytometry Core Facility staff for their help. We also thank MedGenome Inc. for helping with the high-throughput sequencing and data analysis.

Follow the Institutional Biosafety Committee (IBSC) guidelines and use the proper facility to handle lentivirus (BSL-2). Personnel should be appropriately trained in the handling and disposal of lentivirus. This protocol follows the biosafety guidelines of Christian Medical College, Vellore.
1. Selection of the most potent promoter to obtain persistent and prolonged expression of the shRNAs
NOTE: It is essential to perform a transduction experiment using lentiviral vectors with different promoters that express fluorescence proteins to identify the promoter that provides a stable and long-term expression of shRNAs in the cell line selected for the experiment. The most commonly used promoters for this purpose are hEF1&#945; (human elongation factor 1&#945;), hCMV (human cytomegalovirus), and SSFV (spleen focus-forming virus) promoters that express green fluorescence protein (GFP) (Figure 2A).
<ol>
	<li>Perform the cell count using the trypan blue exclusion method. Suspend the MV4-11 AraC R cells in 10% RPMI medium (RPMI with 10% FBS supplemented with 100 U/mL penicillin and 100 U/mL streptomycin) containing 8 &#181;g/mL polybrene. Seed 1 x 106 cells in 1.5 mL of medium/well of a six-well plate in triplicates.</li>
	<li>Add different volumes of concentrated lentiviruses (for example, 10 &#181;L, 20 &#181;L, and 40 &#181;L depending on the titer of the lentiviruses) to each well. Gently swirl the plate to mix the contents.</li>
	<li>Perform spinfection by centrifugation at 920 x g at 37&#160;&#176;C for 90 min.</li>
	<li>Immediately incubate the plates in a CO2 incubator (5% CO2 at 37 &#176;C).</li>
	<li>Observe the GFP expression after 48 h under a fluorescence microscope to ensure successful transduction.</li>
	<li>Quantify the GFP expression by flow cytometry after 72 h to evaluate the transduction efficiency.</li>
	<li>Culture the cells for a total of 2 weeks and monitor the GFP expression periodically by flow cytometry. The reduction in GFP expression measured by the mean fluorescence intensity and the percentage of GFP+ cells indicate promoter silencing.</li>
	<li>Sort the GFP+ cells on the 10th day and culture the cells for 1 week after sorting. Check the GFP expression periodically during the culture.</li>
	<li>Use the untransduced cells to set the gate. A population beyond the 1 x 101 scale toward the right on the x-axis is considered a positive population for GFP.</li>
	<li>Choose the promoter that shows a persistent expression of GFP in the prolonged culture of the cells.</li>
</ol>
2. Preparation of pooled lentiviral human epigenetic factor shRNA library
<ol>
	<li>Culture 293T cells (at a low passage number with a good proliferation rate) in three 10 cm cell culture dishes with 8 mL of 10% DMEM medium (DMEM with 10% FBS containing 100 U/ml penicillin and 100 U/ml streptomycin) in each plate.</li>
	<li>Once the cells attain 60% confluency, replace the medium with a complete medium.</li>
	<li>Prepare the transfection mix (as outlined in Table 1) that contains serum-free medium, lentiviral packaging (psPAX2) and envelope plasmid (pMD2.G), pooled library plasmids, and finally, the transfection reagent.</li>
	<li>Tap the mixture to mix it well and incubate at room temperature for 15 min.</li>
	<li>Add the transfection mixture to the 293T cells and mix gently by swirling the plates. Incubate the plates in a CO2 incubator (5% CO2 at 37 &#176;C). Change the medium after 24 h.</li>
	<li>After 48 h, check for GFP expression under a fluorescence microscope to ensure high transfection efficiency in the 293T cells.</li>
	<li>Collect the virus supernatants after 48 h, 60 h, and 72 h and store them at 4 &#176;C. Add fresh medium (8 mL) at each time point after collecting the virus.</li>
	<li>Pool the virus supernatants. The total volume of the pooled viruses will be: ~8 mL/plate x 3 collections= ~24 mL/plate; ~24 mL/plate x 3 plates = ~72 mL from 3 plates.</li>
	<li>Filter the pooled viruses with the 0.4 &#181;m filter.</li>
	<li>For obtaining a high titer of viruses, concentrate the pooled viruses by ultracentrifugation. Transfer the filtered virus supernatant to two 70 Ti tubes (32 mL/tube) and centrifuge at 18,000x g for 2 h with slow acceleration and deceleration. Use a rotor and centrifuge precooled to 4 &#176;C.</li>
	<li>Take out the tubes from the centrifuge gently and remove the supernatant completely.</li>
	<li>Using a micropipette, gently resuspend the pellet in 400 &#181;L of DMEM (without FBS and antibiotics). Incubate on the ice for 1 h and mix the pellets from both tubes.</li>
	<li>Aliquot the viruses and freeze at &#8722;80 &#176;C. 
	NOTE: The tubes and viruses must be kept on ice during steps 2.10.-2.13. Avoid frothing while resuspending the virus with the plain medium. The medium should be maintained at 4 &#176;C.</li>
	<li>Calculate the concentration (x) of virus as below: 
	Total volume before concentration = 72 mL (72,000 &#181;L) 
	Volume after concentrating = 400 &#181;L 
	The concentration = 72,000/400= 180x</li>
</ol>
3. Estimation of the transduction efficiency of lentiviruses
<ol>
	<li>Transduce 293T cells with the concentrated virus in different volumes (2 &#181;L, 4 &#181;L, 8 &#181;L) to confirm the successful preparation of lentiviruses. 
	NOTE: If concentrated viruses show high transduction efficiencies (&#62;50%) in small volumes (1-2 &#181;L), it is advisable to dilute the concentrated virus in the desired amounts (100x to 75x).</li>
	<li>Dilute the concentrated virus to 100x with DMEM (without FBS and antibiotics) to perform titration experiments in the MV4-11 AraC R cell line.</li>
	<li>Seed 1 x 106 cells (MV4-11 AraC R cell line in 1.5 mL of 10% RPMI medium containing 8 &#181;g/mL polybrene) in one well of a six-well plate. Prepare four wells for transducing with different volumes of viruses.</li>
	<li>Add four different volumes (for example, 1 &#181;L, 1.5 &#181;L, 2 &#181;L, and 2.5 &#181;L) of 100x viruses.</li>
	<li>Perform spinfection by centrifugation of the plate at 920 x g for 90 min at 37 &#176;C.</li>
	<li>After 72 h, measure the percentage of GFP+ cells by flow cytometry.</li>
	<li>Determine the volume of the viruses to obtain 30% transduction efficiency. This low transduction efficiency is to ensure single shRNA integration per cell.</li>
</ol>
4. Transduction of pooled epigenetic shRNA library in the drug-resistant cell line
<ol>
	<li>Maintain the target cell line MV4-11 AraC R at a 0.5 x 106 cells/mL density. Ensure the cells are at the exponential growth phase in culture.</li>
	<li>Calculate the number of cells to be taken for the experiment as below based on the number of viral integrants. 
	Number of cells required for transduction = 5,485 (number of shRNAs) &#215; 500 (fold shRNA representation) / 0.25 (MOI) = 10,982,000 ~11 x 106 cells</li>
	<li>Resuspend 1.1 x 107 cells in 16 mL of 10% RPMI medium.</li>
	<li>Add polybrene (8 &#181;g/mL) and mix well. Then, add the required volume of viruses to obtain 30% transduction efficiency as calculated in the previous section.</li>
	<li>Seed all the cells on a six-well plate at a density of 1 x 106 cells/1.5 mL of 10% RPMI medium per well.</li>
	<li>Centrifuge the plate for 90 min at 920 x g at 37 &#176;C.</li>
	<li>Incubate the plate overnight in a CO2 incubator (5% CO2 at 37&#160;&#176;C).</li>
	<li>After 24 h, change the medium and transfer the transduced cells to a T-75 flask.</li>
	<li>After 48 h, check the GFP under a fluorescence microscope to ensure successful transduction.</li>
	<li>After 72 h, quantitate the percentage of GFP+ cells by flow cytometry.</li>
</ol>
5. Enrichment of GFP positive cells
NOTE: Expand the transduced cells by culturing them at a density of 0.5 x 106 cells/mL for 5-7 days. These cells are a mixed population of transduced and untransduced, selected based on GFP by sorting, as mentioned in the next step.
<ol>
	<li>Perform flow-sorting of GFP+ transduced cells with the flow-sorting settings set as high purity and low yield. Use the untransduced cells to set the gate. A population beyond 1 x 102 toward the right is considered a positive population.</li>
	<li>Collect the sorted cells in 5% RPMI (RPMI with 5% FBS supplemented with 100 U/mL penicillin and 100 U/mL streptomycin).</li>
	<li>Culture the sorted cells in a 10% RPMI medium.</li>
	<li>After 72 h, perform the post-sorting estimation of the percentage of GFP+ cells to ensure &#62;95% sorting efficiency. 
	NOTE: Make sure to obtain ~3-5 x 106 GFP positive cells after sorting to obtain 450x to ~500x representation of the shRNA library.</li>
</ol>
6. Dropout screening to identify epigenetic factors mediating drug resistance
<ol>
	<li>Culture the shRNA library transduced cells in 10% RPMI for up to 5 days. Cryopreserve the transduced cells for future experiments, if required, before the drug treatment.</li>
	<li>Centrifuge 1 x 107 cells in duplicates at 280 x g for 5 min at 25 &#176;C. Discard the supernatant, and store the pellets at &#8722;80 &#176;C. These samples will serve as the baseline reference for the epigenetic shRNA library.</li>
	<li>Culture the remaining transduced cells as duplicates (R1 and R2), each maintained at a cell count that provides a 500x representation of the library.</li>
	<li>Treat one of the duplicates with the drug (10 &#181;M cytarabine) (R2) and the other without the drug treatment (R1).</li>
	<li>Change the medium for the flasks with and without the drug every 72 h. Repeat the medium change 3x for a cumulative drug exposure of 9 days.</li>
	<li>After 9 days of drug treatment, check the viability of the cells by the trypan blue exclusion method.</li>
	<li>Centrifuge the remaining cells at 280 x g for 5 min at room temperature. Resuspend the cells in sterile PBS and wash the cells. Centrifuge at 280 x g for 5 min at room temperature.</li>
	<li>Discard the supernatant and store the pellets at &#8722;80 &#176;C for DNA extraction.</li>
</ol>
7. Amplification of the integrated shRNAs by PCR
<ol>
	<li>Extract DNA from the transduced baseline cells (T) (step 6.2.) and the cells treated with the drug (R2) and untreated with the drug (R1).</li>
	<li>Check the concentration of the sample using a fluorometer.</li>
	<li>Calculate the amount of DNA required for PCR as below: 
	5,485 (no. of shRNAs) &#215; 500 (shRNA representation) &#215; 1 x 6.6&#8722;12 (g/diploid genome) = 15 &#181;g of DNA</li>
	<li>Subject the samples to the first round of PCR. 
	NOTE: The PCR reaction mixture is depicted in Table 2 with the thermal cycler conditions. Details of the primers are provided in Supplementary Table 1.</li>
	<li>Set up multiple reactions containing 850 ng of DNA per tube (for a total of 43 reactions). 
	NOTE: The first round of PCR amplifies the flanking regions of the shRNA resulting in an amplicon size of 397 bp.</li>
	<li>Pool the PCR products and purify the PCR products using 3-4 columns of a standard gel/PCR purification kit. 
	NOTE: Details are provided in Table 3.</li>
	<li>Elute the product in 50 &#181;L of buffer from each column and pool the elutes.</li>
	<li>Quantify the DNA and store it at &#8722;20 &#176;C. 
	NOTE: The reagents are tabulated in Table 4 with the thermal cycler conditions. The second-round PCR utilizes the primers with the INDEX sequence required for multiplexing in NGS in a singleflow cell. So, each sample should be tagged with a different index. Primer sequences are provided in Supplementary Table 1.</li>
	<li>Set up four reactions with 500 ng of the primary PCR product in a total reaction volume of 50 &#181;L for each sample and the negative control.</li>
	<li>Load the entire product for electrophoresis using 2% TBE agarose gel and confirm the band size using a 1 kb molecular ladder. The size of the amplicon is 399 bp.</li>
	<li>Visualize the PCR products on the gel documentation system.</li>
	<li>Excise the specific band and purify using a gel purification kit. 
	NOTE: Calculations for purification with the kit are provided in Table 3.</li>
	<li>Elute in a final volume of 30 &#181;L of elution buffer. The approximate concentration of each eluent will be around 80-90 ng/&#181;L with a total volume of 30 &#181;L.</li>
</ol>
8. Next-generation sequencing and data analysis
<ol>
	<li>Sequence the gel-purified product from step 7.13. on a next-generation sequencer (e.g., Illumina) to obtain the read count for the depleted shRNAs.</li>
	<li>Trim the adaptor sequences of the generated FastQ reads using the Fastx-tool kit (version 0.7).</li>
	<li>Align the filtered reads to reference sequences using a Bowtie aligner with the parameter of allowing two mismatches. Ensure the read length after adapter trimming is around 21 bp.</li>
	<li>Load the files using the Samtools (version 1.9) program. Use the Flagstat option and calculate the alignment summary.</li>
	<li>Load the trimmed fastQ files in CRISPRCloud2 (CC2) software (https://crispr.nrihub.org/) along with the reference shRNA sequences in the form of FASTA files and perform the analysis as per the instructions.
	<ol>
		<li>Click on the Start Analysis link in the CC2.</li>
		<li>Select the screen type as Survival and Dropout screens. Set the number of groups and type the name for each group.</li>
		<li>Upload the reference library in FASTA file format. The data uploaded is processed. Click on the output URL to obtain the results. 
		NOTE: This software uses a beta-binomial model with a modified Student&#39;s t-test to measure differences in sgRNA/shRNA levels, followed by Fisher&#39;s combined probability test to estimate the gene-level significance. It calculates estimated overall log2-fold changes (Log2Fc) of shRNAs for the epigenetic factors between the treated (enriched/depleted) and the untreated samples (baseline) with &#34;p-values&#34; and &#34;FDR values.&#34;</li>
	</ol>
	</li>
	<li>Ensure the FDR value is &#34;Negative&#34; for a Dropout screen and &#34;Positive&#34; for a Survival screen. 
	NOTE: CC2 is an analysis platform for counting the reads and calculating the fold representation (enrichment or depletion) of shRNAs between samples.</li>
	<li>Identify the significantly enriched and depleted shRNAs (FDR &#60;0.05) from the CC2 results. 
	NOTE: There are 5-24 shRNAs against each factor. Check the FDR values for each of the shRNAs; consider the FDR, which is &#60;0.01, and take an average for the selected shRNAs. Consider the top 40 hits. Plot the graph for the selected factors based on Log2Fc or FDR negative values. Make sure the non-targeting shRNAs also have significant FDR values to ensure the authenticity of the data as they serve as control shRNAs.</li>
</ol>

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The authors have no conflicts of interest and nothing to disclose.

RNA interference (RNAi) is extensively used for functional genomics studies, which include siRNA and shRNA screening. The benefit of shRNA is that they can be incorporated into plasmid vectors and integrated into genomic DNA, resulting in stable expression and, thus, more prolonged knockdown of the target mRNA. A pooled shRNA library screening is robust and cost-effective compared to the conventional arrayed screens (siRNA). Identifying the essentiality of a specific class of proteins in a genome-wide screen can be cumbersome. Targeted screening approaches can reveal the essentiality of individual proteins for cancer cell survival or drug resistance within the specific class with reduced noise. This protocol highlights the RNAi screen of epigenetic factors to interrogate both essential and non-essential genes systematically, and it demonstrates the use of this approach as a functional platform allowing the identification of targets responsible for AML drug resistance.
This powerful technology demands certain precautions in the design and execution of the experiments. The first is the choice of promoters that do not undergo significant silencing during the cell culture for the expression of shRNAs as their robust expression is critical for the knockdown of target genes. Second, the maintenance of shRNA representation (the number of cells transduced with each shRNA) is of particular importance. Throughout the shRNA screen, an average representation of &#62;500 cells per shRNA construct augments the potential for identifying the shRNAs that cause phenotypic effects.
The RNAi screening strategy is a competitive growth screen; therefore, it is critical to ensure the target cells are in the logarithmic growth phase and not over-confluent during the screen to minimize loss of representation caused by restricted growth. It is ideal to maintain the cells at 50%-70% confluence to minimize experimental variations. We recommend maintaining consistent lots of media, sera, viral supernatants, drugs, and tissue culture plasticware for all screen replicates to reduce experimental variation.
It is advisable to perform pilot experiments to optimize the transduction efficiency of the lentivirus to calculate the volume of viruses required to achieve one integration of shRNA per cell. To achieve this integration of one shRNA per cell, the transduction efficiency should be less than 30%. As the amount, of viruses to obtain 30% transduced cells may vary between the cell lines used for the experiments, a standardization experiment for each cell line is required. Transduction efficiencies can also be influenced by cell culture conditions and the rate of cell proliferation. Hence, functional titers should be determined in the pilot experiment using the same conditions used in the screening experiment. Maintaining 3-6 replicates is always advisable to get statistically significant hits.
The two major concerns of the shRNA screening approach are the off-target effects and the variable knockdown efficiencies of shRNAs16. These can be overcome by using multiple shRNAs of each gene. Adapting the prokaryotic immune system CRISPR-Cas9 in mammalian cells has allowed easy, efficient, and cost-effective genome editing in cultured human cells. This system has been exploited extensively to perform genome-wide genetic screens similar to those carried out using shRNA. The fate of cells harboring a particular single-guide RNA (sgRNA) sequence can be monitored using deep sequencing. The experimental setup outlined here can be used for CRISPR screening as well.

Therapeutic options in acute myeloid leukemia (AML) have remained unchanged for nearly the past five decades, with cytarabine (AraC) and anthracyclines as the cornerstone for treating the disease. One of the challenges to the success of AML therapy is the resistance of leukemic stem cells to chemotherapy, leading to disease relapse1,2. Epigenetic regulation plays a vital role in cancer pathogenesis and drug resistance, and several epigenetic factors have emerged as promising therapeutic targets3,4,5. Epigenetic regulatory mechanisms affect proliferation and survival under continuous exposure to chemotherapeutic drugs. Studies in non-hematological malignancies have reported that a small fraction of cells that overcome the drug effect undergo various epigenetics modifications, resulting in those cells&#39; survival6,7. However, the role of epigenetic factors in mediating acquired resistance to cytarabine in AML has not been explored.
High-throughput screening is an approach to drug discovery that has gained global importance over time and has become a standard method in different aspects to identify potential targets in cellular mechanisms, for pathway profiling, and at the molecular level8,9. The synthetic lethality concept involves the interaction between two genes where the perturbation of either gene alone is viable but of both genes simultaneously results in the loss of viability10. Exploiting synthetic lethality in cancer treatment could help identify and mechanistically characterize robust synthetic lethal genetic interactions11. We have adopted a combinatorial approach of high-throughput shRNA screening with synthetic lethality to identify the epigenetic factors responsible for acquired cytarabine resistance in AML.
Acute leukemias driven by chromosomal translocation of the mixed-lineage leukemia gene (MLL or KMT2A) are known to be associated with poor survival in patients. The resulting chimeric products of MLL gene rearrangements, i.e., MLL fusion proteins (MLL-FPs), can transform hematopoietic stem/progenitor cells (HSPCs) into leukemic blasts with the involvement of multiple epigenetic factors. These epigenetic regulators constitute a complicated network that dictates the maintenance of the leukemia program and, therefore, could form potential therapeutic targets. In this context, we used the MV4-11 cell line (harboring the MLL fusion gene MLL-AF4 with the FLT3-ITD mutation; termed as MV4-11 P) to develop the acquired cytarabine resistant cell line, termed as MV4-11 AraC R. The cell line was exposed to increasing doses of cytarabine with intermittent recovery from the drug treatment, known as a drug holiday. The half-maximal inhibitory concentration (IC50) was assessed by in vitro cytotoxicity assay.
We used the pooled epigenetic shRNA Library (see Table of Materials) driven by the hEF1a promoter with a pZIP lentiviral backbone. This library comprises shRNAs targeting 407 epigenetic factors. Each factor has 5-24 shRNAs, with a total of 5,485 shRNAs, including five non-targeting control shRNAs. The modified &#34;UltrmiR&#34; miR-30 scaffold has been optimized for efficient primary shRNA biogenesis and expression12,13.
The outline of this experiment is illustrated in Figure 1A. The current protocol focuses on RNAi screening using the epigenetic factor shRNA library in the MV4-11 AraC R cell line (Figure&#160;1B), a suspension cell line. This protocol can be used to screen any targeted library in any drug-resistant cell line of one&#39;s choice. It should be noted that the transduction protocol will be different for adherent cells.

<table><tbody><tr><td>&lt;strong&gt;Reagents&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>100 bp Ladder Hyper Ladder</td><td>BIOLINE</td><td>BIO-33025</td><td></td></tr><tr><td>1kb Ladder Hyper Ladder</td><td>BIOLINE</td><td>BIO-33056</td><td></td></tr><tr><td>Agarose</td><td>Lonza Seachekm</td><td>50004</td><td></td></tr><tr><td>Betaine (5mM)</td><td>Sigma</td><td>B03001VL</td><td></td></tr><tr><td>Boric Acid</td><td>Qualigens</td><td>12005</td><td></td></tr><tr><td>Cell culture plasticware</td><td>Corning</td><td>as appicable</td><td></td></tr><tr><td>Cytosine &amp;beta;-D-arabinofuranoside hydrochloride</td><td>Sigma</td><td>C1768-500MG</td><td></td></tr><tr><td>DMEM</td><td>MP BIO</td><td>91233354</td><td></td></tr><tr><td>DMSO</td><td>Wak Chemie GMBH</td><td>Cryosure DMSO 10ml</td><td></td></tr><tr><td>EDTA</td><td>Sigma</td><td>E5134</td><td></td></tr><tr><td>Ethidium Bromide</td><td>Sigma</td><td>E1510-10 mL</td><td></td></tr><tr><td>Fetal Bovine Serum</td><td>Thermo Fisher Scientific</td><td>16000044</td><td></td></tr><tr><td>Gel/PCR Purification Kit</td><td>MACHEREY-NAGEL</td><td>REF&amp;nbsp;740609.50</td><td></td></tr><tr><td>Gibco- RPMI 1640</td><td>Thermo Fisher Scientific</td><td>23400021</td><td></td></tr><tr><td>Glacial Acetic Acid</td><td>Thermo</td><td>Q11007</td><td></td></tr><tr><td>hCMV GFP Plasmid</td><td>Transomics</td><td>TransOmics Promoter selection KIT</td><td></td></tr><tr><td>hEF1a GFPlasmid</td><td>Transomics</td><td>TransOmics Promoter selection KIT</td><td></td></tr><tr><td>HEK 293T</td><td>ATCC</td><td>CRL-11268</td><td></td></tr><tr><td>HL60 cell line</td><td>ATCC</td><td>CCL-240</td><td></td></tr><tr><td>KOD Hot Start Polymerase</td><td>Merck</td><td>71086</td><td></td></tr><tr><td>Molm13 cell line</td><td>Ellen Weisberg Lab, Dana Farber Cancer Institute, Boston, MA, USA</td><td>Dana Farber Cancer Institute, Boston, MA, USA</td><td></td></tr><tr><td>MV4-11 cell line</td><td>ATCC</td><td>CRL-9591</td><td></td></tr><tr><td>Penicillin streptomycin</td><td>Thermo Fisher Scientific</td><td>15140122</td><td></td></tr><tr><td>psPAX2 and pMD2.G</td><td>Addgene</td><td>Addgene plasmid no.12260 &amp;amp; Addgene plasmid no. 12259</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Invitrogen</td><td>REF Q32854</td><td></td></tr><tr><td>SFFV&amp;nbsp; GFP Plasmid</td><td>Transomics</td><td>TransOmics Promoter selection KIT</td><td></td></tr><tr><td>shERWOOD-UltrmiR shRNA Library from Transomics</td><td>Transomics</td><td>Cat No. TLH UD7409; Lot No: A116.V 132.14</td><td></td></tr><tr><td>Trans-IT-LTI Mirus</td><td>Mirus</td><td>Mirus Catalog Number: MIR2300</td><td></td></tr><tr><td>Tris</td><td>MP Biomedicals</td><td>0219485591</td><td></td></tr><tr><td>Trypan Blue</td><td>Sigma-Aldrich</td><td>T8154-100ML</td><td></td></tr><tr><td>Ultra centrifuge Tubes</td><td>Beckman Coulter</td><td>&amp;nbsp;103319</td><td></td></tr><tr><td>&lt;strong&gt;Equipments&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>5% CO&lt;sub&gt;2&lt;/sub&gt; incubator</td><td>Thermo Fisher</td><td></td><td></td></tr><tr><td>BD Aria III cell sorter</td><td>Becton Dickinson</td><td></td><td></td></tr><tr><td>Beckman Coulter Optima L-100K- Ultracentrifugation</td><td>Beckman coulter</td><td></td><td></td></tr><tr><td>Centrifuge</td><td>Thermo Multiguge 3SR+</td><td></td><td></td></tr><tr><td>ChemiDoc Imaging system&amp;nbsp;(Fluro Chem M system)</td><td>Fluro Chem</td><td></td><td></td></tr><tr><td>Leica AF600</td><td>Leica</td><td></td><td></td></tr><tr><td>Light Microscope</td><td>Zeiss Axiovert 40c</td><td></td><td></td></tr><tr><td>Nanodrop</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>Qubit 3.0 Fluorometer</td><td>Invitrogen</td><td></td><td></td></tr><tr><td>Thermal Cycler</td><td>BioRad</td><td></td><td></td></tr></tbody></table>

pooled shrna library screening to identify factors that modulate a drug resistance phenotype

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve survival in patients with acute myeloid leukemia (AML). A small fraction of leukemic cells that survive chemotherapy have a poised epigenetic state to tolerate chemotherapeutic insult. Further exposure to chemotherapy allows these drug persister cells to attain a fixed epigenetic state, which leads to altered gene expression, resulting in the proliferation of these drug-resistant populations and eventually relapse or refractory disease. Therefore, identifying epigenetic modulations that necessitate the survival of drug-resistant leukemic cells is critical. We detail a protocol to identify epigenetic modulators that mediate resistance to the nucleoside analog cytarabine (AraC) using pooled shRNA library screening in an acquired cytarabine-resistant AML cell line. The library consists of 5,485 shRNA constructs targeting 407 human epigenetic factors, which allows high-throughput epigenetic factor screening.

The overall screening workflow is depicted in Figure 1A. In vitro cytotoxicity of the MV4-11 AraC R (48 h) revealed the IC50 to cytarabine in the MV4-11 AraC R to be higher than the MV4-11 P (Figure 1B). This cell line was used in the study as the model for screening the epigenetic factors responsible for cytarabine resistance.
Figure 2A shows the linearized pZIP vector maps with three different promoters: hEF1&#945; (human elongation factor 1&#945;), hCMV (human cytomegalovirus), and SSFV (spleen focus-forming virus), with the scrambled shRNA within the UltramiR sequence. Figure 2B illustrates the pZIP vector map used in the library experiment and the shRNA targeting the epigenetic factor with Zsgreen and puromycin as the selectable marker driven by the hEF1a promoter. These vectors were used in the selection of the most potent promoter experiment.
The selection of the most potent promoter to get a consistent and prolonged expression in the target cells is illustrated in Figure 3. The quantification of GFP measured by MFI showed more than 90% transduction efficiency at the end of 72 h in MV4-11 AraC R for all three promoters. The histogram for GFP expression shows a heterogeneous population for hCMV but a homogeneous one in the case of hEF1a and SFFV at day 3 of transduction (Figure 3A). The bar graph shows the GFP expression driven by three different promoters (hEF1&#945;, hCMV, and SFFV) at day 3 of transduction in MV4-11 AraC R (Figure 3B). SFFV-driven GFP showed a reduction in MFI on day 5 of transduction (Figure 3C). No decrease in MFI was observed in the hEF1a-driven GFP transduced MV4-11 parental line post sorting. Thus, the hEF1a promoter was identified as a suitable promoter for the cell line (Figure 3D).
Figure 4A illustrates the transfection efficiency of the pooled shRNA library plasmids in 293T cells post 48 h of transfection. The GFP expression was bright, indicating good transfection efficiency. Post virus collection, the transduction efficiency of the prepared pooled virus was checked in 293T cells in three different concentrations (2 &#181;L, 4 &#181;L, and 8 &#181;L). We observed that even 2 &#181;L virus resulted in the same efficiency as that of 8 &#181;L virus, thereby confirming the high viral titer (Figure 4B).
Figure 5 illustrates the pooled library transduction in the MV4-11 AraC R cell line and the determination of the lentiviral titer. The pooled shRNA library lentivirus was diluted 100x and then added to the MV4-11 AraC R cell line in different volumes (1 &#181;L, 1.5 &#181;L, 2 &#181;L, and 2.5 &#181;L). The efficiency was checked at the end of 72 h. The transduction efficiency was 30% for 2-2.5 &#181;L of the virus, confirming one shRNA integration per cell (Figure 5A). Transduction with 2.5&#956;l of pooled library virus in 1.1 x 107&#160;MV4-11 AraC R cells resulted in 30% transduction efficiency. GFP-positive cells were sorted, representing the pooled shRNA library (Figure 5B).
After the transduction of pooled shRNA epigenetic lentivirus in the MV4-11 AraC R cell line, the GFP-positive cells were sorted on day 5 or day 7 (Figure 6). These sorted cells were subjected to prolonged exposure to cytarabine for 9 days. The viability was checked on the 9th day, and the cells were collected for DNA. (Figure 6A). The bar graph shows the reduction in the proliferation rate of the transduced cells in the presence of cytarabine (Figure 6B).
The extracted DNA from the samples was subjected to two rounds of PCR, and the final gel eluted product represented the enriched shRNAs quantified by NGS. Figure 7A shows the binding regions of the primer used in the 1st and the 2nd round of PCR, where the 1st round primers bind to the flanking regions of the integrated shRNA, and the 2nd forward primer binds to the loop sequence. The reverse primer binds to a region of the amplified vector in the 1st round of PCR. Figure 7B and Figure 7C illustrate the band size of the PCR product at the end of 1st (397 bp) and the 2nd round PCR (399 bp), which was gel-eluted, purified, and submitted for NGS analysis.
After subjecting the DNA to a standard quality control procedure, the samples were processed for NGS analysis. We used CRISPRCloud2, a user-friendly, cloud-based analysis platform for identifying enriched and depleted shRNAs in the pooled shRNA screening. Figure 8 illustrates the representation of enriched or depleted shRNAs targeting epigenetic factors that could mediate cytarabine resistance in AML.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63383/63383fig01.jpg" /> 
Figure 1: Outline and model of the study. (A) Illustration of the overview of the workflow. (B) MV4-11 parental and AraC-resistant cells treated with increasing cytarabine concentrations (0.1 &#181;M to 1000 &#181;M) and viability-assessed by MTT assay. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63383/63383fig02.jpg" /> 
Figure 2. Illustration of the linearized vectors. (A) The three vector maps with three different promoters, hEF1&#945; (human elongation factor 1&#945;), hCMV (human cytomegalovirus), and SSFV (spleen focus-forming virus), with the scrambled shRNA within the UltramiR sequence. (B) Illustration of the vector map used in the library experiment with hEF1&#945; promoter and the shRNA targeting the epigenetic factor with Zsgreen and puromycin as the selectable marker. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63383/63383fig03.jpg" /> 
Figure 3: Selection of the most potent promoter to obtain persistent and prolonged expression of the shRNAs. (A) Representative flow plots for GFP quantified by flow cytometry at the end of 72 h for hEF1a, hCMV, and SFFV promoters. hCMV shows a heterogeneous peak, while hEF1a and SFFV show a single homogenous peak. (B) Promoter efficiency in the MV4-11 AraC R cell line.&#160;(C)&#160;SFFV-driven GFP shows silencing of GFP in prolonged culture. (D) hEF1a-driven GFP cells show sustained expression post sorting of the cells. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63383/63383fig04.jpg" /> 
Figure 4: Checking the efficiency of the prepared pooled shRNA lentivirus. (A) Fluorescence microscopy images show the transfection efficiency of the pooled shRNA library transfected in 293T at the end of 48 h using 10x magnification. (B)&#160;The transduction efficiency of the pooled virus was assessed in 293T cells with varying virus volumes (2&#181;L, 4&#181;L, and 8&#181;L) using 10x magnification. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63383/63383fig05.jpg" /> 
Figure 5: Pooled library transduction in the target cell line and determination of lentiviral titer. (A) MV4-11 AraC R cell line transduced with varying volumes of virus (1&#181;L, 1.5&#181;L, 2&#181;L, and 2.5&#181;L). The efficiency was checked at the end of 72 h. (B) Transduction of pooled library virus in 1 x 107&#160;MV4-11 AraC R cells to achieve 30% transduction efficiency, then sorting of GFP-positive cells. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63383/63383fig06.jpg" /> 
Figure 6: Dropout screening to identify epigenetic factors mediating drug resistance. (A)&#160;Schematic illustration of drug treatment for the enrichment of resistant cells.Library infected cells were subjected to prolonged cytarabine exposure for 9 days, followed by checking the viability and collecting the cells for DNA extraction. (B)&#160;Reduction in viability to prolonged cytarabine exposure in resistant cell lines. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63383/63383fig07.jpg" /> 
Figure 7: Preparation of the amplicons representing the integrated shRNA. (A) The binding regions of the primer used in the 1st and the 2nd round of PCR, where the 1st round primers bind to the flanking regions of the integrated shRNA and the second forward primer binds to the loop sequence. The reverse binds to a region of the amplified vector region in the 1st PCR. (B) Illustration of the band size of the PCR product at the end of 1st round PCR (397 bp). (C)&#160;The 2nd round PCR product (399bp) was gel-eluted, purified, and given for NGS. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/63383/63383fig08.jpg" /> 
Figure 8: Screening results in the AML cell line (MV-4-11 Ara-C R cell line). After subjecting the DNA to a standard quality control procedure, the samples were processed for NGS analysis. We used CRISPRCloud2, a user-friendly, cloud-based analysis platform, to identify enriched and depleted shRNAs in the pooled shRNA screening. &#8239;The figure represents enriched or depleted shRNAs targeting epigenetic factors that could mediate cytarabine resistance in AML. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/63383fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagents</td>
			<td>Amount to be added</td>
			<td>Volume to be added</td>
		</tr>
		<tr>
			<td>OptiMEM Serum free medium</td>
			<td>1 ml</td>
			<td></td>
		</tr>
		<tr>
			<td>PP1-&#160; psPAX2 (1&#181;g/&#181;L)</td>
			<td>4 &#181;g</td>
			<td>4 &#181;g/1&#181;g=4 &#181;L</td>
		</tr>
		<tr>
			<td>PP2- pMD2.G (1&#181;g/&#181;L)</td>
			<td>4 &#181;g</td>
			<td>4 &#181;g/1&#181;g=4 &#181;L</td>
		</tr>
		<tr>
			<td>shRNA Library</td>
			<td>10,000 ng</td>
			<td>10000 ng/721ng= 13.8 &#181;L</td>
		</tr>
		<tr>
			<td>Transfection Reagent TransIT-LTI</td>
			<td>30 &#181;L</td>
			<td>30 &#181;L</td>
		</tr>
	</tbody>
</table>
Table 1: Preparation of Transfection Plasmid Mix (Calculation for 10cm plate) <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/shRNA screen manuscript_Jove_Table 1 revised27022022.zip" target="_blank">Please click here to download this Table.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagents&#160;</td>
			<td>Amount for 1reaction&#160;</td>
		</tr>
		<tr>
			<td>10X KOD Buffer</td>
			<td>5&#181;L&#160;</td>
		</tr>
		<tr>
			<td>DNTPs (2.5mM)</td>
			<td>5&#181;L</td>
		</tr>
		<tr>
			<td>MgCl2 (25mM)</td>
			<td>4&#181;L</td>
		</tr>
		<tr>
			<td>Betaine (5mM)</td>
			<td>5&#181;L</td>
		</tr>
		<tr>
			<td>Forward Primer (10uM)</td>
			<td>1.7&#181;L</td>
		</tr>
		<tr>
			<td>Reverse Primer (10uM)</td>
			<td>1.7&#181;L</td>
		</tr>
		<tr>
			<td>KOD Hot Start Polymerase&#160;</td>
			<td>1.5&#181;L</td>
		</tr>
		<tr>
			<td>gDNA (850ng)</td>
			<td></td>
		</tr>
		<tr>
			<td>H20</td>
			<td></td>
		</tr>
		<tr>
			<td>Total&#160;</td>
			<td>50&#181;L</td>
		</tr>
	</tbody>
</table>
<img alt="Table 2" src="/files/ftp_upload/63383/63383tbl2v2.jpg" /> 
Table 2: PCR reaction mixture for 1st Round of PCR <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/shRNA screen manuscript_Jove_Table 2 revised.zip" target="_blank">Please click here to download this Table.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Sample</td>
			<td>Total Volume from Pooled PCR reaction</td>
			<td>Amount of NT1 solution&#160;</td>
			<td>Column used&#160;</td>
		</tr>
		<tr>
			<td>Sample</td>
			<td>1ml</td>
			<td>2ml</td>
			<td>1</td>
		</tr>
		<tr>
			<td>PCR Purification: Mix 1 volume of the sample with 2 volumes of Buffer NT1&#160;</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Column capacity- 700 &#181;L</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4"></td>
		</tr>
		<tr>
			<td>Sample</td>
			<td>Total Volume from Pooled PCR reaction</td>
			<td>Amount of NT1 solution&#160;</td>
			<td>Column used&#160;</td>
		</tr>
		<tr>
			<td>Gel containing DNA&#160;</td>
			<td>For each 100 mg of agarose gel &#60;2%&#160;</td>
			<td>200 &#181;L</td>
			<td>1</td>
		</tr>
	</tbody>
</table>
Table 3: Calculations for purification of the products of 1st of PCR <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/shRNA screen manuscript_Jove_Table 3 revised.zip" target="_blank">Please click here to download this Table.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Reagents&#160;</td>
			<td>Calculations for one reaction&#160;</td>
		</tr>
		<tr>
			<td>10X KOD Buffer</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>DNTP Mix&#160;</td>
			<td>5 &#181;L</td>
		</tr>
		<tr>
			<td>MgSO4&#160; (25 mM)</td>
			<td>4 &#181;L</td>
		</tr>
		<tr>
			<td>Betaine (5 mM)</td>
			<td>2 &#181;L</td>
		</tr>
		<tr>
			<td>Secondary Forward Primer (10 &#181;M)</td>
			<td>7.5 &#181;L</td>
		</tr>
		<tr>
			<td>Secondary Reverse Primer (10 &#181;M)</td>
			<td>7.5 &#181;L</td>
		</tr>
		<tr>
			<td>KOD Polymerase&#160;</td>
			<td>1.5 &#181;L</td>
		</tr>
		<tr>
			<td>Pooled Primary PCR DNA (500 ng)</td>
			<td></td>
		</tr>
		<tr>
			<td>H20</td>
			<td></td>
		</tr>
		<tr>
			<td>Total&#160;</td>
			<td>50 &#181;L&#160;</td>
		</tr>
	</tbody>
</table>
<img alt="Table 4" src="/files/ftp_upload/63383/63383tbl4v2.jpg" /> 
Table 4: PCR reaction mixture for 2nd Round of PCR <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63383/shRNA screen manuscript_Jove_Table 4 revised.zip" target="_blank">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype at JoVE.com

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

High-throughput RNA interference (RNAi) screening using a pool of lentiviral shRNAs can be a tool to detect therapeutically relevant synthetic lethal targets in malignancies. We provide a pooled shRNA screening approach to investigate the epigenetic effectors in acute myeloid leukemia (AML).

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve ...

pooled-shrna-library-screening-to-identify-factors-that-modulate-drug

Nanyang Technological University

Research

JoVE Journal

Cancer Research

3.2K Views.  Christian Medical College. Het algemene doel van de volgende video is om het gebruik van de gepoolde shRNA epigenetische bibliotheek aan te tonen om een negatieve selectiescreening uit te voeren. Deze screening maakt het mogelijk om specifieke epigenetische doelen te identificeren door middel van sequencing. Deze procedure kan helpen bij het identificeren van de epigenetische factoren die verantwoordelijk zijn voor het bemiddelen van verworven cytarabineresistentie in AML De stappen zijn als volgt.Selectie van d...

Video: Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance can be caused by a range of mechanisms, including increased drug elimination, decreased drug uptake, drug inactivation and alterations of drug targets. Recent data showed that other than by well-known genetic (mutation, amplification) and epigenetic (DNA hypermethylation, histone post-translational modification) modifications, drug resistance mechanisms might also be regulated by splicing aberrations. This is a rapidly growing field of investigation that deserves future attention in order to plan more effective therapeutic approaches. The protocol described in this paper is aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of several in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes. In particular, we evaluated the differential splicing of DDX5 and PKM transcripts. The aberrant splicing detected by the computational tool MATS was validated in leukemic cells, showing that different DDX5 splice variants are expressed in the parental vs. resistant cells. In these cells, we also observed a higher PKM2/PKM1 ratio, which was not detected in the Panc-1 gemcitabine-resistant counterpart compared to parental Panc-1 cells, suggesting a different mechanism of drug-resistance induced by gemcitabine exposure.

Using RNA-sequencing to Detect Novel Splice Variants Related to Drug Resistance in In Vitro Cancer Models

Here we describe a protocol aimed at investigating the impact of aberrant splicing on drug resistance in solid tumors and hematological malignancies. To this goal, we analyzed the transcriptomic profiles of parental and resistant in vitro models through RNA-seq and established a qRT-PCR based method to validate candidate genes.

Drug resistance remains a major problem in the treatment of cancer for both hematological malignancies and solid tumors. Intrinsic or acquired resistance ...

Looking for Driver Pathways of Acquired Resistance to Targeted Therapy: Drug Resistant Subclone Generation and Sensitivity Restoring by Gene Knock-down

The past two decades have seen a shift from cytotoxic drugs to targeted therapy in medical oncology. Although targeted therapeutic agents have shown more impressive clinical efficacy and minimized adverse effects than traditional treatments, drug resistance has become the main limitation to their benefits. Several preclinical in vitro/in vivo models of acquired resistance to targeted agents in clinical practice have been developed mainly by using two strategies: i) genetic manipulation for modeling genotypes of acquired resistance, and ii) in vitro/in vivo selection of resistant models. In the present work, we propose a unifying framework, for investigating the underlying mechanisms responsible for acquired resistance to targeted therapeutic agents, starting from the generation of drug-resistant cellular subclones to the description of silencing procedures used for restoring the sensitivity to the inhibitor. This simple time- and cost-effective approach is widely applicable, and could be easily extended to investigate resistance mechanisms to other targeted therapeutic drugs in different tumor histotypes.

This is a time- and cost-effective in vitro protocol investigating the mechanisms of acquired resistance to targeted therapeutic agents, which is a highly unmet medical need in cancer management.

The past two decades have seen a shift from cytotoxic drugs to targeted therapy in medical oncology. Although targeted therapeutic agents have shown more ...

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control in model organisms. Technologies including short hairpin RNAs (shRNAs) and clustered regularly interspaced short palindromic repeats (CRISPR) have enabled such screens in diploid mammalian cells. Here we describe a large-scale shRNA screen for regulators of X-chromosome inactivation (XCI), using a murine cell line with firefly luciferase and hygromycin resistance genes knocked in at the C-terminus of the methyl CpG binding protein 2 (MeCP2) gene on the inactive X-chromosome (Xi). Reactivation of the construct in the reporter cell line conferred survival advantage under hygromycin B selection, enabling us to screen a large shRNA library and identify hairpins that reactivated the reporter by measuring their post-selection enrichment using next-generation sequencing. The enriched hairpins were then individually validated by testing their ability to activate the luciferase reporter on Xi.

We report a small hairpin RNA (shRNA) and next generation sequencing-based protocol for identifying regulators of X-chromosome inactivation in a murine cell line with firefly luciferase and hygromycin resistance genes fused to the methyl CpG binding protein 2 (MeCP2) gene on the inactive X chromosome.

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control ...

Genetics

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

Here we describe a protocol for employing high-throughput RNAi screening to uncover host targets that can be manipulated to enhance oncolytic virus therapy, specifically rhabodvirus and vaccinia virus therapy, but it can be readily adapted to other oncolytic virus platforms or for discovering host genes that modulate virus replication generally.

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus ...

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries

CCCTC-binding factor (CTCF)-mediated stable topologically associating domains (TADs) play a critical role in constraining interactions of DNA elements that are located in neighboring TADs. CTCF plays an important role in regulating the spatial and temporal expression of HOX genes that control embryonic development, body patterning, hematopoiesis, and leukemogenesis. However, it remains largely unknown whether and how HOX loci associated CTCF boundaries regulate chromatin organization and HOX gene expression. In the current protocol, a specific sgRNA pooled library targeting all CTCF binding sites in the HOXA/B/C/D loci has been generated to examine the effects of disrupting CTCF-associated chromatin boundaries on TAD formation and HOX gene expression. Through CRISPR-Cas9 genetic screening, the CTCF binding site located between HOXA7/HOXA9 genes (CBS7/9) has been identified as a critical regulator of oncogenic chromatin domain, as well as being important for maintaining ectopic HOX gene expression patterns in MLL-rearranged acute myeloid leukemia (AML). Thus, this sgRNA library screening approach provides novel insights into CTCF mediated genome organization in specific gene loci and also provides a basis for the functional characterization of the annotated genetic regulatory elements, both coding and noncoding, during normal biological processes in the post-human genome project era.

HOX Loci Focused CRISPR/sgRNA Library Screening Identifying Critical CTCF Boundaries

A CRISPR/sgRNA library has been applied to interrogating protein-coding genes.However, the feasibility of a sgRNA library to uncover the function of a CTCF boundary in gene regulation remains unexplored. Here, we describe a HOX loci specific sgRNA library to elucidate the function of CTCF boundaries in HOX loci.

CCCTC-binding factor (CTCF)-mediated stable topologically associating domains (TADs) play a critical role in constraining interactions of DNA elements ...

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Genome editing using the CRISPR-Cas system has vastly advanced the ability to precisely edit the genomes of various organisms. In the context of mammalian cells, this technology represents a novel means to perform genome-wide genetic screens for functional genomics studies. Libraries of guide RNAs (sgRNA) targeting all open reading frames permit the facile generation of thousands of genetic perturbations in a single pool of cells that can be screened for specific phenotypes to implicate gene function and cellular processes in an unbiased and systematic way. CRISPR-Cas screens provide researchers with a simple, efficient, and inexpensive method to uncover the genetic blueprints for cellular phenotypes. Furthermore, differential analysis of screens performed in various cell lines and from different cancer types can identify genes that are contextually essential in tumor cells, revealing potential targets for specific anticancer therapies. Performing genome-wide screens in human cells can be daunting, as this involves the handling of tens of millions of cells and requires analysis of large sets of data. The details of these screens, such as cell line characterization, CRISPR library considerations, and understanding the limitations and capabilities of CRISPR technology during analysis, are often overlooked. Provided here is a detailed protocol for the successful performance of pooled genome-wide CRISPR-Cas9 based screens.

CRISPR-Cas9 technology provides an efficient method to precisely edit the mammalian genome in any cell type and represents a novel means to perform genome-wide genetic screens. A detailed protocol discussing the steps required for the successful performance of pooled genome-wide CRISPR-Cas9 screens is provided here.

Genome editing using the CRISPR-Cas system has vastly advanced the ability to precisely edit the genomes of various organisms. In the context of mammalian ...

Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

Transcription factors can alter the expression of numerous target genes that influence a variety of downstream processes making them good targets for anti-cancer therapies. However, directly targeting transcription factors is often difficult and can cause adverse side effects if the transcription factor is necessary in one or more adult tissues. Identifying upstream regulators that aberrantly activate transcription factors in cancer cells offers a more feasible alternative, particularly if these proteins are easy to drug. Here, we describe a protocol that can be used to combine arrayed medium-scale lentiviral libraries and a dual-luciferase-based transcriptional reporter assay to identify novel regulators of transcription factors in cancer cells. Our approach offers a quick, easy, and inexpensive way to test hundreds of genes in a single experiment. To demonstrate the use of this approach, we performed a screen of an arrayed lentiviral RNAi library containing several regulators of Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ), two transcriptional co-activators that are the downstream effectors of the Hippo pathway. However, this approach could be modified to screen for regulators of virtually any transcription factor or co-factor and could also be used to screen CRISPR/CAS9, cDNA, or ORF libraries.

To identify novel regulators of transcription factors, we developed an approach to screen arrayed lentiviral or retroviral RNAi libraries using a dual-luciferase-based transcriptional reporter assay. This approach offers a quick and relatively inexpensive way to screen hundreds of candidates in a single experiment.

Transcription factors can alter the expression of numerous target genes that influence a variety of downstream processes making them good targets for ...

Genome-Scale Screening to Identify and Compare Therapeutic Targets in Tumors

Genetic Profiling and Genome-Scale Dropout Screening to Identify Therapeutic Targets in Mouse Models of Malignant Peripheral Nerve Sheath Tumor

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome neurofibromatosis type 1 (NF1), MPNSTs are the most common malignancy and the leading cause of death. These rare and aggressive soft-tissue sarcomas offer a stark future, with 5-year disease-free survival rates of 34-60%. Treatment options for individuals with MPNSTs are disappointingly limited, with disfiguring surgery being the foremost treatment option. Many once-promising therapies such as tipifarnib, an inhibitor of Ras signaling, have failed clinically. Likewise, phase II clinical trials with erlotinib, which targets the epidermal growth factor (EFGR), and sorafenib, which targets the vascular endothelial growth factor receptor (VEGF), platelet-derived growth factor receptor (PDGF), and Raf, in combination with standard chemotherapy, have also failed to produce a response in patients. 
In recent years, functional genomic screening methods combined with genetic profiling of cancer cell lines have proven useful for identifying essential cytoplasmic signaling pathways and the development of target-specific therapies. In the case of rare tumor types, a variation of this approach known as cross-species comparative oncogenomics is increasingly being used to identify novel therapeutic targets. In cross-species comparative oncogenomics, genetic profiling and functional genomics are performed in genetically engineered mouse (GEM) models and the results are then validated in the rare human specimens and cell lines that are available. 
This paper describes how to identify candidate driver gene mutations in human and mouse MPNST cells using whole exome sequencing (WES). We then describe how to perform genome-scale shRNA screens to identify and compare critical signaling pathways in mouse and human MPNST cells and identify druggable targets in these pathways. These methodologies provide an effective approach to identifying new therapeutic targets in a variety of human cancer types.

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens 

We have developed a cross-species comparative oncogenomics approach utilizing genomic analyses and functional genomic screens to identify and compare therapeutic targets in tumors arising in genetically engineered mouse models and the corresponding human tumor type.

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are derived from Schwann cells or their precursors. In patients with the tumor susceptibility syndrome ...

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

Medicine

MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

RNA interference (RNAi) is an intrinsic cellular mechanism for the regulation of gene expression. Harnessing the innate power of this system enables us to knockdown gene expression levels in loss of gene function studies.
There are two main methods for performing RNAi. The first is the use of small interfering RNAs (siRNAs) that are chemically synthesized, and the second utilizes short-hairpin RNAs (shRNAs) encoded within plasmids 1. The latter can be transfected into cells directly or packaged into replication incompetent lentiviral particles. The main advantages of using lentiviral shRNAs is the ease of introduction into a wide variety of cell types, their ability to stably integrate into the genome for long term gene knockdown and selection, and their efficacy in conducting high-throughput loss of function screens. To facilitate this we have created the LentiPlex pooled shRNA library.
The MISSION LentiPlex Human shRNA Pooled Library is a genome-wide lentiviral pool produced using a proprietary process. The library consists of over 75,000 shRNA constructs from the TRC collection targeting 15,000+ human genes 2. Each library is tested for shRNA representation before product release to ensure robust library coverage. The library is provided in a ready-to-use lentiviral format at titers of at least 5 x 108 TU/ml via p24 assay and is pre-divided into ten subpools of approximately 8,000 shRNA constructs each. Amplification and sequencing primers are also provided for downstream target identification.
Previous studies established a synergistic antitumor activity of TRAIL when combined with Paclitaxel in A549 cells, a human lung carcinoma cell line 3, 4. In this study we demonstrate the application of a pooled LentiPlex shRNA library to rapidly conduct a positive selection screen for genes involved in the cytotoxicity of A549 cells when exposed to TRAIL and Paclitaxel. One barrier often encountered with high-throughput screens is the cost and difficulty in deconvolution; we also detail a cost-effective polyclonal approach utilizing traditional sequencing.

Here we use a human LentiPlex pooled library and traditional sequencing methods to identify gene targets promoting cell survival. We demonstrate how to set up and deconvolute a LentiPlex screen and validate the results.

RNA interference (RNAi) is an intrinsic cellular mechanism for the regulation of gene expression. Harnessing the innate power of this system enables us to ...

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

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