Dataset Information

Gene expression is stable in a complete CIB1 knockout keratinocyte model.

ABSTRACT: Epidermodysplasia verruciformis (EV) is a genodermatosis characterized by the inability of keratinocytes to control cutaneous ?-HPV infection and a high risk for non-melanoma skin cancer (NMSC). Bi-allelic loss of function variants in TMC6, TMC8, and CIB1 predispose to EV. The correlation between these proteins and ?-HPV infection is unclear. Its elucidation will advance the understanding of HPV control in human keratinocytes and development of NMSC. We generated a cell culture model by CRISPR/Cas9-mediated deletion of CIB1 to study the function of CIB1 in keratinocytes. Nine CIB1 knockout and nine mock control clones were generated originating from a human keratinocyte line. We observed small changes in gene expression as a result of CIB1 knockout, which is consistent with the clearly defined phenotype of EV patients. This suggests that the function of human CIB1 in keratinocytes is limited and involves the restriction of ?-HPV. The presented model is useful to investigate CIB1 interaction with ?-HPV in future studies.

SUBMITTER: Imahorn E

PROVIDER: S-EPMC7486891 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gene expression is stable in a complete CIB1 knockout keratinocyte model.

Imahorn Elias E Aushev Magomet M Herms Stefan S Hoffmann Per P Cichon Sven S Reichelt Julia J Itin Peter H PH Burger Bettina B

Scientific reports 20200911 1

Epidermodysplasia verruciformis (EV) is a genodermatosis characterized by the inability of keratinocytes to control cutaneous β-HPV infection and a high risk for non-melanoma skin cancer (NMSC). Bi-allelic loss of function variants in TMC6, TMC8, and CIB1 predispose to EV. The correlation between these proteins and β-HPV infection is unclear. Its elucidation will advance the understanding of HPV control in human keratinocytes and development of NMSC. We generated a cell culture model by CRISPR/C ...[more]

PMID: 32917957

Similar Datasets

Project description:Background The study of gene essentiality, which measures the importance of a gene for cell division and survival, is used for the identification of cancer drug targets and understanding of tissue-specific manifestation of genetic conditions. In this work, we analyze essentiality and gene expression data from over 900 cancer lines from the DepMap project to create predictive models of gene essentiality. Methods We developed machine learning algorithms to identify those genes whose essentiality levels are explained by the expression of a small set of “modifier genes”. To identify these gene sets, we developed an ensemble of statistical tests capturing linear and non-linear dependencies. We trained several regression models predicting the essentiality of each target gene, and used an automated model selection procedure to identify the optimal model and hyperparameters. Overall, we examined linear models, gradient boosted trees, Gaussian process regression models, and deep learning networks. Results We identified nearly 3000 genes for which we accurately predict essentiality using gene expression data of a small set of modifier genes. We show that both in the number of genes we successfully make predictions for, as well as in the prediction accuracy, our model outperforms current state-of-the-art works. Conclusions Our modeling framework avoids overfitting by identifying the small set of modifier genes, which are of clinical and genetic importance, and ignores the expression of noisy and irrelevant genes. Doing so improves the accuracy of essentiality prediction in various conditions and provides interpretable models. Overall, we present an accurate computational approach, as well as interpretable modeling of essentiality in a wide range of cellular conditions, thus contributing to a better understanding of the molecular mechanisms that govern tissue-specific effects of genetic disease and cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-023-01446-6.

Dataset Information

Gene expression is stable in a complete CIB1 knockout keratinocyte model.

Publications

Gene expression is stable in a complete CIB1 knockout keratinocyte model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets