Dataset Information

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

ABSTRACT: A central remaining question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into ‘co-essential’ pathways, but this approach has been limited by ubiquitous false positives. Here, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and protein complexes and predicts the functions of 155 uncharacterized genes. Validating top predictions, we show that TMEM189 encodes plasmanylethanolamine desaturase, the long-sought key enzyme for plasmalogen synthesis. We also show that C15orf57 binds the AP2 complex, localizes to clathrin-coated pits, and enables efficient transferrin uptake. Finally, we provide an interactive web tool for the community to explore our results. Our results establish co-essentiality profiling as a powerful resource for biological pathway identification and discovery of novel gene functions.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Permanent Cell Line Cell, Cell Culture

DISEASE(S): Cervix Carcinoma

SUBMITTER: Roarke Kamber

LAB HEAD: Michael Bassik

PROVIDER: PXD023558 | Pride | 2022-02-16

REPOSITORIES: Pride

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Publications

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes.

Wainberg Michael M Kamber Roarke A RA Balsubramani Akshay A Meyers Robin M RM Sinnott-Armstrong Nasa N Hornburg Daniel D Jiang Lihua L Chan Joanne J Jian Ruiqi R Gu Mingxin M Shcherbina Anna A Dubreuil Michael M MM Spees Kaitlyn K Meuleman Wouter W Snyder Michael P MP Bassik Michael C MC Kundaje Anshul A

Nature genetics 20210415 5

A central question in the post-genomic era is how genes interact to form biological pathways. Measurements of gene dependency across hundreds of cell lines have been used to cluster genes into 'co-essential' pathways, but this approach has been limited by ubiquitous false positives. In the present study, we develop a statistical method that enables robust identification of gene co-essentiality and yields a genome-wide set of functional modules. This atlas recapitulates diverse pathways and prote ...[more]

PMID: 33859415

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Project description:The process for evaluating chemical safety is inefficient, costly, and animal intensive. There is growing consensus that the current process of safety testing needs to be significantly altered to improve efficiency and reduce the number of untested chemicals. In this study, the use of short-term gene expression profiles was evaluated for predicting the increased incidence of mouse lung tumors. Animals were exposed to a total of 26 diverse chemicals with matched vehicle controls over a period of three years. Upon completion, significant batch-related effects were observed. Adjustment for batch effects significantly improved the ability to predict increased lung tumor incidence. For the best statistical model, the estimated predictive accuracy under honest five-fold cross-validation was 79.3% with a sensitivity and specificity of 71.4 and 86.3%, respectively. A learning curve analysis demonstrated that gains in model performance reached a plateau at 25 chemicals, indicating that the size of the current data set was sufficient to provide a robust classifier. The classification results showed a small subset of chemicals contributed disproportionately to the misclassification rate. For these chemicals, the misclassification was more closely associated with genotoxicity status than efficacy in the original bioassay. Statistical models were also used to predict dose-response increases in tumor incidence for methylene chloride and naphthalene. The average posterior probabilities for the top models matched the results from the bioassay for methylene chloride. For naphthalene, the average posterior probabilities for the top models over-predicted the tumor response, but the variability in predictions were significantly higher. The study provides both a set of gene expression biomarkers for predicting chemically-induced mouse lung tumors as well as a broad assessment of important experimental and analysis criteria for developing microarray-based predictors of safety-related endpoints. Experiment Overall Design: Five-week-old female B6C3F1 mice were exposed for 13 weeks to 26 chemicals. The chemical and dose information are provided with the individual sample annotations. With each chemical treatment, a matched vehicle control group was run concurrently with the exposure. A subset of two chemicals, methylene chloride and naphthalene, were performed in a five-point dose response with a matched purified air control group. The concentrations for these exposures overlapped those in the original cancer bioassay. Gavage exposures were administered 5 days per week and feed exposures were provided 7 days per week. For inhalation exposures, mice were exposed 6 hr per day, 5 days per week. After 13 weeks, animals were euthanized and lungs were collected. The right lobe was used for microarray analysis. Microarray analysis was performed on the lungs of three to four mice per treatment group.

Dataset Information

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes

Publications

A genome-wide atlas of co-essential modules assigns function to uncharacterized genes.

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