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. 2018 Jan 23;9(11):9838-9851.
doi: 10.18632/oncotarget.24305. eCollection 2018 Feb 9.

Unbiased compound-protein interface mapping and prediction of chemoresistance loci through forward genetics in haploid stem cells

Affiliations

Unbiased compound-protein interface mapping and prediction of chemoresistance loci through forward genetics in haploid stem cells

Moritz Horn et al. Oncotarget. .

Abstract

Forward genetic screens in haploid mammalian cells have recently emerged as powerful tools for the discovery and investigation of recessive traits. Use of the haploid system provides unique genetic tractability and resolution. Upon positive selection, these screens typically employ analysis of loss-of-function (LOF) alleles and are thus limited to non-essential genes. Many relevant compounds, including anti-cancer therapeutics, however, target essential genes, precluding positive selection of LOF alleles. Here, we asked whether the use of random and saturating chemical mutagenesis might enable screens that identify essential biological targets of toxic compounds. We compare and contrast chemical mutagenesis with insertional mutagenesis. Selecting mutagenized cells with thapsigargin, an inhibitor of the essential Ca2+ pump SERCA2, insertional mutagenesis retrieved cell clones overexpressing SERCA2. With chemical mutagenesis, we identify six single amino acid substitutions in the known SERCA2-thapsigargin binding interface that confer drug resistance. In a second screen, we used the anti-cancer drug MG132/bortezomib (Velcade), which inhibits proteasome activity. Using chemical mutagenesis, we found 7 point mutations in the essential subunit Psmb5 that map to the bortezomib binding surface. Importantly, 4 of these had previously been identified in human tumors with acquired bortezomib resistance. Insertional mutagenesis did not identify Psmb5 in this screen, demonstrating the unique ability of chemical mutagenesis to identify relevant point mutations in essential genes. Thus, chemical mutagenesis in haploid embryonic stem cells can define the interaction of toxic small molecules with essential proteins at amino acid resolution, fully mapping small molecule-protein binding interfaces.

Keywords: chemoresistance prediction; forward genetic screens; haploid stem cells; interaction site mapping; target identification.

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Conflict of interest statement

CONFLICTS OF INTEREST None.

Figures

Figure 1
Figure 1. Insertional and ENU-based mutagenesis in mouse haploid embryonic stem cells
Schematic representation of experimental workflow for both screening approaches. LOF loss-of-function; GOF gain-of-function.
Figure 2
Figure 2. Thapsigargin-resistance screen using insertional mutagenesis validates Atp2a2/SERCA2 overexpression as resistance mechanism
(A) Schematic representation of the SERCA2 Ca2+-ATPase. (B) Schematic representation of the experimental workflow for thapsigargin-resistance screen using insertional mutagenesis. Confirmation and analysis includes assessment of resistance dependence on insertion orientation followed by insertion site mapping. (C) Analysis of thapsigargin resistance with respect to the gene trapping cassette orientation. Green and grey bars indicate clones in which the gene trapping positions affected thapsigargin resistance. Green bars indicate clones in which genomic insertion sites were successfully mapped. (D) Schematic representation of genomic integration sites from thapsigargin-resistant colonies (green triangles). Only clones with thapsigargin resistance linked to the trapping cassette orientation were analyzed by inverse PCR. (E) qRT-PCR analysis of thapsigargin-resistant clones with gene trapping insertions upstream of the Atp2a2 gene. Relative Atp2a2 mRNA levels from ≥3 repeats (Mean ± SEM) are shown. ***p < 0.001, **p < 0.01 (ANOVA).
Figure 3
Figure 3. Chemical mutagenesis screen for thapsigargin resistance reveals amino acid substitutions in Atp2a2/SERCA2
(A) Schematic representation of the experimental workflow for thapsigargin-resistance screen using chemical mutagenesis. (B) Table listing candidate suppressor genes for thapsigargin-resistance identified by whole exome sequencing. Genes are sorted by the number of hits recovered after standard filtering (≥7% mutant allele frequency) per kbp. (C) Schematic representation of the SERCA amino acid sequence. Substitutions inferred from whole exome sequencing analysis with low stringency (≥3% mutant allele frequency) and their positions are highlighted. (D) Structure of rabbit SERCA2 (grey) in complex with thapsigargin (blue). Identified substitutions are highlighted in red. pdb:5a3q. (E) Genotyping (Sanger sequencing) of CRISPR/Cas9 engineered Atp2a2/SERCA2 alterations and their consequences at the amino acid level. (F) Representative images of WT cells and two engineered Atp2a2/SERCA2 suppressor candidates after 48 hours of treatment with 30 nM thapsigargin or respective control. Scale bar, 100 µm. (G) Cell viability assay (XTT) of cells with the Atp2a2/SERCA2 thapsigargin-suppressor candidate mutations engineered by CRISPR/Cas9 genome editing, and unaltered WT controls. **p < 0.01 (ANOVA). Mean ± SEM (n = 3).
Figure 4
Figure 4. MG132 resistance screen using chemical mutagenesis identifies suppressor mutations in PSMB5 and maps the PSMB5-MG132 binding interface
(A) Schematic representation of proteasome inhibitors MG132 and bortezomib targeting the β5 subunit (PSMB5). (B) Schematic representation of the experimental workflow for MG132/bortezomib-resistance screening using insertional mutagenesis. No clone showed MG132 resistance linked to the gene trapping insertion. (C) Schematic representation of the experimental workflow for MG132/bortezomib-resistance screening using chemical mutagenesis. (D) Table showing the only candidate suppressor gene for MG132-resistance identified by whole exome sequencing. The number of hits recovered after standard filtering (≥7% mutant allele frequency) per kbp is displayed. (E) Schematic representation of the PSMB5 amino acid sequence. Substitutions inferred from whole exome sequencing analysis with low stringency (≥3% mutant allele frequency) and their positions are highlighted. (F) Structure of yeast PSMB5 (grey) in complex with MG132 (green). Identified substitutions are highlighted in red. pdb:5d0t. (G) Genotyping (Sanger sequencing) of CRISPR/Cas9 engineered Psmb5 candidate mutations and their consequences at the amino acid level. (H) Representative images of WT cells and cells carrying two engineered Psmb5 suppressor candidate mutations after a 48 hours treatment with 0.5 µM MG132, 50 nM bortezomib, or respective control. Scale bar, 100 µm. (I) Cell viability assay (XTT) of Psmb5 MG132-suppressor candidates engineered by CRISPR/Cas9 genome editing and WT controls. *p < 0.05, **p < 0.01, ***p < 0.001 (ANOVA). Mean ± SEM (n = 3).

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