Dataset Information

CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.

ABSTRACT:

Introduction

Genes contain multiple promoters that can drive the expression of various transcript isoforms. Although transcript isoforms from the same gene could have diverse and non-overlapping functions, current loss-of-function methodologies are not able to differentiate between isoform-specific phenotypes.

Results

Here, we show that CRISPR interference (CRISPRi) can be adopted for targeting specific promoters within a gene, enabling isoform-specific loss-of-function genetic screens. We use this strategy to test functional dependencies of 820 transcript isoforms that are gained in gastric cancer (GC). We identify a subset of GC-gained transcript isoform dependencies, and of these, we validate CIT kinase as a novel GC dependency. We further show that some genes express isoforms with opposite functions. Specifically, we find that the tumour suppressor ZFHX3 expresses an isoform that has a paradoxical oncogenic role that correlates with poor patient outcome.

Conclusions

Our work finds isoform-specific phenotypes that would not be identified using current loss-of-function approaches that are not designed to target specific transcript isoforms.

SUBMITTER: Davies R

PROVIDER: S-EPMC7836456 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.

Davies Rebecca R Liu Ling L Taotao Sheng S Tuano Natasha N Chaturvedi Richa R Huang Kie Kyon KK Itman Catherine C Mandoli Amit A Qamra Aditi A Hu Changyuan C Powell David D Daly Roger J RJ Tan Patrick P Rosenbluh Joseph J

Genome biology 20210126 1

<h4>Introduction</h4>Genes contain multiple promoters that can drive the expression of various transcript isoforms. Although transcript isoforms from the same gene could have diverse and non-overlapping functions, current loss-of-function methodologies are not able to differentiate between isoform-specific phenotypes.<h4>Results</h4>Here, we show that CRISPR interference (CRISPRi) can be adopted for targeting specific promoters within a gene, enabling isoform-specific loss-of-function genetic sc ...[more]

PMID: 33499898

Similar Datasets

Project description:BACKGROUND:Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging. RESULTS:We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~?2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes. CONCLUSIONS:Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Dataset Information

CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.

Introduction

Results

Conclusions

Publications

CRISPRi enables isoform-specific loss-of-function screens and identification of gastric cancer-specific isoform dependencies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets