METTL3 regulates alternative splicing of cell cycle-related genes via crosstalk between mRNA m6A modifications and splicing factors.

N6-methyladenosine (m6A) modification in RNA affects various aspects of RNA metabolism and regulates gene expression. This modification is modulated by many regulatory proteins, such as m6A methyltransferases (writers), m6A demethylases (erasers), and m6A-binding proteins (readers). Previous studies have suggested that alterations in m6A regulatory proteins induce genome-wide alternative splicing in many cancer cells. However, the functional effects and molecular mechanisms of m6A-mediated alternative splicing have not been fully elucidated. To understand the consequences of this modification on RNA splicing in cancer cells, we performed RNA sequencing and analyzed alternative splicing patterns in METTL3-knockdown osteosarcoma U2OS cells. We detected 1,803 alternatively spliced genes in METTL3-knockdown cells compared to the controls and found that cell cycle-related genes were enriched in differentially spliced genes. A comparison of the published MeRIP-seq data for METTL14 with our RNA sequencing data revealed that 70-87% of alternatively spliced genes had an m6A peak near 1 kb of alternative splicing sites. Among the 19 RNA-binding proteins enriched in alternative splicing sites, as revealed by motif analysis, expression of SFPQ highly correlated with METTL3 expression in 12,839 TCGA pan-cancer patients. We also found that cell cycle-related genes were enriched in alternatively spliced genes of other cell lines with METTL3 knockdown. Taken together, we suggest that METTL3 regulates m6A-dependent alternative splicing, especially in cell cycle-related genes, by regulating the functions of splicing factors such as SFPQ.

The feasibility of granzyme B levels using an amperometric immunosensor for lung cancer detection.

Background: Low-dose computed tomography (LDCT) has improved the early detection of lung cancer. However, LDCT scans present several disadvantages, including the abundance of false-positive results, which lead to a high socioeconomic cost, psychological burden, and repeated exposure to radiation. Therefore, the identification of complementary biomarkers is needed to select high-risk individuals for LDCT. Here, we showed that granzyme B testing with the novel immunosensor has diagnostic value for identifying patients with lung cancer. Methods: We enrolled 44 patients with lung cancer and 51 health controls at Pusan National University Yangsan Hospital in Korea between March 2018 and September 2019. The immunosensor analyzed serum granzyme B levels, and their association with lung cancer detection was evaluated with machine learning models. Results: Serum granzyme B levels were assessed in samples from patients with lung cancer and healthy individuals. Granzyme B testing showed 100% sensitivity, 80% specificity, and an area under the curve of 0.938 for lung cancer detection. After combining granzyme B testing with clinical predictors such as age, smoking status, or pack-years, results from the five-fold cross-validation with random forest model improved diagnostic accuracy of 92.1%, with a sensitivity, specificity, and area under the curve of 92.0%, 92.1%, and 0.977, respectively. Conclusions: This feasibility study suggested that granzyme B may be utilized to detect lung cancer.

3D graph contrastive learning for molecular property prediction.

MOTIVATION: Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, but a few issues exist. (i) Existing SSL models are large-scale; there is a limitation to implementing SSL where the computing resource is insufficient. (ii) In most cases, they do not utilize 3D structural information for molecular representation learning. The activity of a drug is closely related to the structure of the drug molecule. Nevertheless, most current models do not use 3D information or use it partially. (iii) Previous models that apply contrastive learning to molecules use the augmentation of permuting atoms and bonds. Therefore, molecules having different characteristics can be in the same positive samples. We propose a novel contrastive learning framework, small-scale 3D Graph Contrastive Learning (3DGCL) for molecular property prediction, to solve the above problems. RESULTS: 3DGCL learns the molecular representation by reflecting the molecule's structure through the pretraining process that does not change the semantics of the drug. Using only 1128 samples for pretrain data and 0.5 million model parameters, we achieved state-of-the-art or comparable performance in six benchmark datasets. Extensive experiments demonstrate that 3D structural information based on chemical knowledge is essential to molecular representation learning for property prediction. AVAILABILITY AND IMPLEMENTATION: Data and codes are available in https://github.com/moonkisung/3DGCL.