
DDX3Xのセリン584のオ-GlcNAc化が、ユビキチン-プロテアソーム系による分解から DDX3Xを保護し、細胞周期の進行を制御する。


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O-GlcNAcylation is an evolutionary conserved post-translational modification implicated in neurodevelopment. Missense variants of O-GlcNAc transferase (OGT) are causal for the intellectual disability syndrome OGT Congenital Disorder of Glycosylation (OGT-CDG). The observation of microcephaly in OGT-CDG patients suggests that dysregulation of the cell cycle and aberrant neurogenesis may contribute to disease aetiology. Here, we identify Ser584 O-GlcNAcylation of DDX3X, a known intellectual disability and microcephaly associated protein, as a key regulator of G1/S-phase transition, inhibiting proteasome-dependent degradation of DDX3X. DDX3X levels are reduced in a mouse model of OGT-CDG, alongside the DDX3X-target gene and synaptogenic regulator cyclin E1. These data reveal how a single DDX3X O-GlcNAc site exerts control of the cell cycle and highlights dysregulation of DDX3X-dependent translation, and concomitant impairments in cortical neurogenesis, as a possible pathway disrupted in OGT-CDG.

### Competing Interest Statement

The authors have declared no competing interest.

O-GlcNAcylation of the intellectual disability protein DDX3X exerts proteostatic cell cycle control

知的障害タンパク質ddx3xのオ-glcnac化が細胞周期制御を行う


知的障害タンパク質DDX3Xのオ-GlcNAc化が細胞周期制御を行う


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This study presents a comprehensive, automated method for studying the cell cycle in both non-adherent and adherent cells, offering a valuable tool for cellular biology, cancer research, and drug development.


Understanding the details of the cell cycle at the level of individual cells is critical for both cellular biology and cancer research. While existing methods using specific fluorescent markers have advanced our ability to study the cell cycle in cells that adhere to surfaces, there is a clear gap when it comes to non-adherent cells. In this study, we combine a specialized surface to improve cell attachment, the genetically-encoded FUCCI(CA)2 sensor, an automated image processing and analysis pipeline, and a custom machine-learning algorithm. This combined approach allowed us to precisely measure the duration of different cell cycle phases in non-adherent, as well as adherent cells.

Our method provided detailed information from hundreds of cells under different experimental conditions in a fully automated manner. We validated this approach in two different acute myeloid leukemia cell lines, NB4 and Kasumi-1, which have unique and distinct cell cycle characteristics. We also measured how drugs that influence cell cycle properties affect the duration of each phase in the cell cycles of these cell lines. Importantly, our cell cycle analysis system is freely available and has also been validated for use with adherent cells.

In summary, this article introduces a comprehensive, automated method for studying the cell cycle in both non-adherent and adherent cells, offering a valuable tool for cellular biology, cancer research and drug development.

### Competing Interest Statement

The authors have declared no competing interest.

Automated workflow for the cell cycle analysis of non-adherent and adherent cells using a machine learning approach

automated-cell-cycle-analysis-of-non-adherent-and-adherent-cells-using-a-machine-learning-approach


Automated Cell Cycle Analysis of Non-Adherent and Adherent Cells Using a Machine Learning Approach
