Search

Predicted functional consequence of FANCJ mutations using in silico programs

Information on FANCJ mutation library, including source of mutation

Sensitivity data from validated FANCJ LOF mutations

Controls of mutation screen

FANCJ (BRIP1/BACH1) is a hereditary breast and ovarian cancer (HBOC) gene encoding a DNA helicase. Similar to HBOC genes, BRCA1 and BRCA2, FANCJ is critical for processing DNA inter-strand crosslinks (ICL) induced by chemotherapeutics, such as cisplatin. Consequently, cells deficient in FANCJ or its catalytic activity are sensitive to ICL-inducing agents. Unfortunately, the majority of FANCJ clinical mutations remain uncharacterized, limiting therapeutic opportunities to effectively use cisplatin to treat tumors with mutated FANCJ. Here, we sought to perform a comprehensive screen to identify FANCJ loss-of-function (LOF) mutations. We developed a FANCJ lentivirus mutation library representing approximately 450 patient–derived FANCJ nonsense and missense mutations to introduce FANCJ mutants into FANCJ knockout (K/O) HeLa cells. We performed a high-throughput screen to identify FANCJ LOF mutants that, as compared with wild-type FANCJ, fail to robustly restore resistance to ICL-inducing agents, cisplatin or mitomycin C (MMC). On the basis of the failure to confer resistance to either cisplatin or MMC, we identified 26 missense and 25 nonsense LOF mutations. Nonsense mutations elucidated a relationship between location of truncation and ICL sensitivity, as the majority of nonsense mutations before amino acid 860 confer ICL sensitivity. Further validation of a subset of LOF mutations confirmed the ability of the screen to identify FANCJ mutations unable to confer ICL resistance. Finally, mapping the location of LOF mutations to a new homology model provides additional functional information.Implications:We identify 51 FANCJ LOF mutations, providing important classification of FANCJ mutations that will afford additional therapeutic strategies for affected patients.

Figure Legends

Validation of mutation screen

Functional prediction of FANCJ mutations by Mutation Assessor

Predicted Functional consequence of LOF mutations based on homology model

Sequence alignment of human, mouse, and chicken FANCJ

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery:https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available athttps://phenaid.ardigen.com/jumpcpexplorer/.

FANCJ (BRIP1/BACH1) is a hereditary breast and ovarian cancer (HBOC) gene encoding a DNA helicase. Similar to HBOC genes, BRCA1 and BRCA2, FANCJ is critical for processing DNA inter-strand crosslinks (ICL) induced by chemotherapeutics, such as cisplatin. Consequently, cells deficient in FANCJ or its catalytic activity are sensitive to ICL-inducing agents. Unfortunately, the majority of FANCJ clinical mutations remain uncharacterized, limiting therapeutic opportunities to effectively use cisplatin to treat tumors with mutated FANCJ. Here, we sought to perform a comprehensive screen to identify FANCJ loss-of-function (LOF) mutations. We developed a FANCJ lentivirus mutation library representing approximately 450 patient–derived FANCJ nonsense and missense mutations to introduce FANCJ mutants into FANCJ knockout (K/O) HeLa cells. We performed a high-throughput screen to identify FANCJ LOF mutants that, as compared with wild-type FANCJ, fail to robustly restore resistance to ICL-inducing agents, cisplatin or mitomycin C (MMC). On the basis of the failure to confer resistance to either cisplatin or MMC, we identified 26 missense and 25 nonsense LOF mutations. Nonsense mutations elucidated a relationship between location of truncation and ICL sensitivity, as the majority of nonsense mutations before amino acid 860 confer ICL sensitivity. Further validation of a subset of LOF mutations confirmed the ability of the screen to identify FANCJ mutations unable to confer ICL resistance. Finally, mapping the location of LOF mutations to a new homology model provides additional functional information. Implications: We identify 51 FANCJ LOF mutations, providing important classification of FANCJ mutations that will afford additional therapeutic strategies for affected patients.

Identifying genetic and chemical perturbations with similar impacts on cell morphology can reveal compounds’ mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here, we create such a Resource dataset, CPJUMP1, where each perturbed gene is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same gene, and we find that identifying matches between chemical perturbations and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling, such as functional genomics and drug discovery.

Pooled variant expression libraries can test the phenotypes of thousands of variants of a gene in a single multiplexed experiment. In a library encoding all single-amino-acid substitutions of a protein, each variant differs from its reference only at a single codon-position located anywhere along the coding sequence. Consequently, accurately identifying these variants by sequencing is a major technical challenge. A popular but expensive brute-force approach is to divide the pool of variants into multiple smaller sub-libraries that each contains variants of a small region and that must each be constructed and screened individually, but that can then be PCR-amplified and fully sequenced with a single read to allow direct readout of variant abundance. Here we present an approach to screen very large variant libraries with mutations spanning a wide region in a single pool, including library design criteria and mutant-detection algorithms that permit reliable calling and counting of variants from large-scale sequencing data.

A key question in genome research is whether biologically active proteins are restricted to the ∼20,000 canonical, well-annotated genes, or rather extend to the many non-canonical open reading frames (ORFs) predicted by genomic analyses. To address this, we experimentally interrogated 553 ORFs nominated in ribosome profiling datasets. Of these 553 ORFs, 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines, 257 (46%) showed evidence of protein translation when ectopically expressed in HEK293T cells, and 401 (73%) induced gene expression changes measured by transcriptional profiling following ectopic expression across 4 cell types. CRISPR tiling and start codon mutagenesis indicated that the biological effects of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We selected one of these ORFs,G029442--renamedGREP1(Glycine-Rich Extracellular Protein-1)--for further characterization. We found thatGREP1encodes a secreted protein highly expressed in breast cancer, and its knock-out in 263 cancer cell lines showed preferential essentiality in breast cancer derived lines. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect ofGREP1knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

The brain is the foremost non-gonadal tissue for expression of non-coding RNAs of unclear function. Yet, whether such transcripts are truly non-coding or rather the source of non-canonical protein translation is unknown. Here, we used functional genomic screens to establish the cellular bioactivity of non-canonical proteins located in putative non-coding RNAs or untranslated regions of protein-coding genes. We experimentally interrogated 553 open reading frames (ORFs) identified by ribosome profiling for three major phenotypes: 257 (46%) demonstrated protein translation when ectopically expressed in HEK293T cells, 401 (73%) induced gene expression changes following ectopic expression across 4 cancer cell types, and 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines. CRISPR tiling and start codon mutagenesis indicated that the biological impact of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We functionally characterized one of these ORFs, G029442—renamed GREP1 (Glycine-Rich Extracellular Protein-1)—as a cancer-implicated gene with high expression in multiple cancer types, such as gliomas. GREP1 knockout in >200 cancer cell lines reduced cell viability in multiple cancer types, including glioblastoma, in a cell-autonomous manner and produced cell cycle arrest via single-cell RNA sequencing. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect of GREP1 knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.