The key cyclin-dependent kinase Cdk1 (Cdc2) promotes irreversible mitotic entry, mainly by activating the phosphatase Cdc25 while suppressing the tyrosine kinase Wee1. Wee1 needs to be downregulated at the onset of mitosis to ensure rapid activation of Cdk1. In human somatic cells, one mechanism of suppressing Wee1 activity is mediated by ubiquitylation-dependent proteolysis through the Skp1/Cul1/F-box protein (SCF) ubiquitin E3 ligase complex. This mechanism is believed to be conserved from yeasts to humans. So far, the best-characterized human F-box proteins involved in recognition of Wee1 are β-TrCP (BTRCP) and Tome-1 (CDCA3). Although fission yeast Wee1 was the first identified member of its conserved kinase family, the F-box proteins involved in recognition and ubiquitylation of Wee1 have not been identified in this organism. In this study, our screen using Wee1- luciferase as the reporter revealed that two F-box proteins, Pof1 and Pof3, are required for downregulating Wee1 and are possibly responsible for recruiting Wee1 to SCF. Our genetic analyses supported a functional relevance between Pof1 and Pof3 and the rate of mitotic entry, and Pof3 might play a major role in this process.

Objective: To investigate the feasibility of dynamic computed tomography in recording and describing the spatial motion characteristics of the arytenoid cartilage. Methods: Dynamic computed tomography recorded the real-time motion trajectory of the arytenoid cartilage during inspiration and phonation. A stationary coordinate system was established with the cricoid cartilage as a reference and a motion coordinate system was established using the movement of the arytenoid cartilage. The Euler angles of the arytenoid cartilage movement were calculated by transformation of the two coordinate systems, and the spatial motion characteristics of the arytenoid cartilage were quantitatively studied. Results: Displacement of the cricoid cartilage was primarily inferior during inspiration. During phonation, the displacement was mainly superior. When the glottis closed, the superior displacement was about 5-8 mm within 0.56 s. During inspiration, the arytenoid cartilage was displaced superiorly approximately 1-2 mm each 0.56 s. The rotation angle was subtle with slight rotation around the XYZ axis, with a range of 5-10 degrees. During phonation, the displacement of the arytenoid cartilage was mainly inferior (about 4-6 mm), anterior (about 2-4 mm) and medial (about 1-2 mm). The motion of the arytenoid cartilage mainly consisted of medial rolling, and there was an alternating movement of anterior-posterior tilting. The arytenoid cartilage rolled medially (about 20-40 degrees within 0.56 s), accompanied by anterior-posterior tilting (about 15-20 degrees within 0.56 s). Conclusion: Dynamic computed tomography recordings of arytenoid cartilage movement can be combined with Euler transformations as a tool to study the spatial characteristics of laryngeal structures during phonation.

Single-cell RNA-sequencing (scRNA-seq) has enabled transcriptome-wide profiling of gene expressions in individual cells. A myriad of computational methods have been proposed to learn cell-cell similarities and/or cluster cells, however, high variability and dropout rate inherent in scRNA-seq confounds reliable quantification of cell-cell associations based on the gene expression profile alone. Lately bioinformatics studies have emerged to capture key transcriptome information on alternative polyadenylation (APA) from standard scRNA-seq and revealed APA dynamics among cell types, suggesting the possibility of discerning cell identities with the APA profile. Complementary information at both layers of APA isoforms and genes creates great potential to develop cost-efficient approaches to dissect cell types based on multiple modalities derived from existing scRNA-seq data without changing experimental technologies. We proposed a toolkit called scLAPA for learning association for single-cell transcriptomics by combing single-cell profiling of gene expression and alternative polyadenylation derived from the same scRNA-seq data. We compared scLAPA with seven similarity metrics and five clustering methods using diverse scRNA-seq datasets. Comparative results showed that scLAPA is more effective and robust for learning cell-cell similarities and clustering cell types than competing methods. Moreover, with scLAPA we found two hidden subpopulations of peripheral blood mononuclear cells that were undetectable using the gene expression data alone. As a comprehensive toolkit, scLAPA provides a unique strategy to learn cell-cell associations, improve cell type clustering and discover novel cell types by augmentation of gene expression profiles with polyadenylation information, which can be incorporated in most existing scRNA-seq pipelines. scLAPA is available at https://github.com/BMILAB/scLAPA.

No abstract

As a surveillance mechanism, the activated spindle assembly checkpoint (SAC) potently inhibits the E3 ubiquitin ligase APC/C (anaphase-promoting complex/ cyclosome) to ensure accurate chromosome segregation. Although the protein phosphatase 2A (PP2A) has been proposed to be both, directly and indirectly, involved in spindle assembly checkpoint inactivation in mammalian cells, whether it is similarly operating in the fission yeast Schizosaccharomycer pombe has never been demonstrated. Here, we investigated whether fission yeast PP2A is involved in SAC silencing by following the rate of cyclin B (Cdc13) destruction at SPBs during the recovery phase in nda3-KM311 cells released from the inhibition of APC/C by the activated spindle checkpoint. The timing of the SAC inactivation is only slightly delayed when two B56 regulatory subunits (Par1 and Par2) of fission yeast PP2A are absent. Overproduction of individual PP2A subunits either globally in the nda3-KM311 arrest-and-release system or locally in the synthetic spindle checkpoint activation system only slightly suppresses the SAC silencing defects in PP1 deletion (dis2Δ) cells. Our study thus demonstrates that the fission yeast PP2A is not a key regulator actively involved in SAC inactivation.