A simplified transposon mutagenesis method to perform phenotypic forward genetic screens in cultured cells

Abstract

Background The introduction of genome-wide shRNA and CRISPR libraries has facilitated cell-based screens to identify loss-of-function mutations associated with a phenotype of interest. Approaches to perform analogous gain-of-function screens are less common, although some reports have utilized arrayed viral expression libraries or the CRISPR activation system. However, a variety of technical and logistical challenges make these approaches difficult for many labs to execute. In addition, genome-wide shRNA or CRISPR libraries typically contain of hundreds of thousands of individual engineered elements, and the associated complexity creates issues with replication and reproducibility for these methods. Results Here we describe a simple, reproducible approach using the SB transposon system to perform phenotypic cell-based genetic screens. This approach employs only three plasmids to perform unbiased, whole-genome transposon mutagenesis. We also describe a ligation-mediated PCR method that can be used in conjunction with the included software tools to map raw sequence data, identify candidate genes associated with phenotypes of interest, and predict the impact of recurrent transposon insertions on candidate gene function. Finally, we demonstrate the high reproducibility of our approach by having three individuals perform independent replicates of a mutagenesis screen to identify drivers of vemurafenib resistance in cultured melanoma cells. Conclusions Collectively, our work establishes a facile, adaptable method that can be performed by labs of any size to perform robust, genome-wide screens to identify genes that influence phenotypes of interest. Electronic supplementary material The online version of this article (10.1186/s12864-019-5888-6) contains supplementary material, which is available to authorized users.

A Genetic Screen to Identify Gain- and Loss-of-Function Modifications that Enhance T-cell Infiltration into Tumors

“…To identify transposon insertion sites in T cells, bulk genomic DNA was isolated from snap-frozen tumor and spleen tissues of T-cell-mutagenized mice using GenElute Mammalian Genome DNA miniprep Kit (Sigma). DNA was sheared using the Covaris E220 sonicator, and DNA fragments containing transposongenomic sequences were amplified via ligation-mediated PCR and submitted for sequencing at the Iowa Institute of Human Genetics on Illumina Hi-Seq2000/4000 as previously described (14)(15)(16). The detailed protocol for sequencing library generation and sequence data analysis is available in Additional File 2 from Feddersen and colleagues (16).…”

“…Transgenic strain data for each individual mouse can be found in Supplementary Table S1. Insertion sites were mapped to GRCm38 using a previously described Integration Analysis System (IAS) pipeline (16), which outputted the intermediate Source Data gff3 file containing all mapped insertion sites. Identification of gene-level transposon-induced driver mutations (gene-level common insertion site, gCIS) was performed using 5,000 bp as the input promoter region size (16).…”

“…Insertion sites were mapped to GRCm38 using a previously described Integration Analysis System (IAS) pipeline (16), which outputted the intermediate Source Data gff3 file containing all mapped insertion sites. Identification of gene-level transposon-induced driver mutations (gene-level common insertion site, gCIS) was performed using 5,000 bp as the input promoter region size (16). All tools and accompanying documentation can be acquired through GitHub (https://github.com/addupuy/IAS.git) Average enrichment scores were calculated by comparing normalized read abundance of individual insertion sites from individual mice in tumors compared with spleen.…”

Transmicron: accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

“…Gene-based methods do not rely on arbitrarily determined window sizes ( 24 , 25 ), do not require cumbersome attribution of the detected CISs to genes ( 26 , 27 ), and may reduce the multiple testing burden, potentially increasing sensitivity. However, being limited to annotated elements ( 28 ), they are less flexible regarding the scale and the scope of the CISs to be analysed. Of note, despite the discrete nature of insertion count data, none of the existing gene-based approaches use count distributions.…”

On the Structure of Thailandene A: Synthetic Examination of the Cryptic Natural Product Aided by a Theoretical Approach

“…4 Presently, our attention has been drawn towards the non-isoprenoid, linear, polyene natural product assigned as thailandene A (1), exhibiting antibacterial and antifungal activity against strains of Staphylococcus aureus and Saccharomyces cerevisiae. 5 Using phenotype-guided transposon mutagenesis as a tool to induce silent bacterial gene clusters, 6 several novel compounds containing a consecutive progression of 1,2-disubstituted alkene monomers have been isolated from the bacterium Burkholderia thailandensis (Figure 1). However, since only milligram quantities were available by the cultivating process, 5 further biological evaluation could be advanced with a synthetic approach.…”