The Roles of Septins in Regulating Fission Yeast Cytokinesis.

Cytokinesis is required to separate two daughter cells at the end of mitosis, and septins play crucial roles in many aspects of cytokinesis. While septins have been intensively studied in many model organisms, including the budding yeast Saccharomyces cerevisiae, septins have been relatively less characterized in the fission yeast Schizosaccharomyces pombe, which has proven to be an excellent model organism for studying fundamental cell biology. In this review, we summarize the findings of septins made in fission yeasts mainly from four aspects: the domain structure of septins, the localization of septins during the cell cycle, the roles of septins in regulating cytokinesis, and the regulatory proteins of septins.

Novel model based on ultrasound predict axillary lymph node metastasis in breast cancer.

BACKGROUND: Whether there is axillary lymph node metastasis is crucial for formulating the treatment plan for breast cancer. Currently, invasive methods are still used for preoperative evaluation of lymph nodes. If non-invasive preoperative evaluation can be achieved, it will effectively improve the treatment plan. OBJECTIVE: Constructed a predict model based on ultrasound examination, which forest axillary lymph node metastasis in breast cancer, and validated this model. METHOD: Patients admitted to Xiamen First Hospital from April 2018 to August 2021 with complete case data were included in this study. Patients who had undergone breast cancer resection and axillary lymph node dissection or sentinel lymph node biopsy were divided into a training and validation cohort in a 7:3 ratio. In the training cohort, patients were divided into metastatic and non-metastatic groups based on whether axillary lymph nodes had metastasis. The parameters of the two groups were compared, and statistically significant parameters were included in multivariate analysis. Then, a Nomogram model was constructed, named Lymph metastasis predict model (LMPM). Calibration curves, receiver operating curve (ROC), and decision curve analysis (DCA) were plotted between the training and validation cohort, calculate the risk score of each patient, identify the optimal cutoff value, and test the predictive efficacy of LMPM. RESULT: Two hundred seventy-three patients were enrolled in final study, the average age 49.7 ± 8.7, training cohort included 191 patients, the diameter of breast cancer, the lymph node peak systolic flow velocity (LNPS) and the cortex area hilum ratio (CH) of lymph node were exist significant difference in metastatic and non-metastatic group. Multivariate analysis showed cancer diameter, LNPS and CH included in LMPM, the cutoff value was 95, the calibration curve, ROC, DCA in training and validation cohort show satisfactory result. CONCLUSION: The predict model-LMPM, can predict axillary lymph node metastasis in breast cancer, which is useful for developing personalized treatment plans. However, further validation of the model is required by incorporating a larger number of patients.

The Cdc42 GAP Rga6 promotes monopolar outgrowth of spores.

The molecular mechanisms underlying the establishment of the monopolar growth of fission yeast spores have been less characterized. Here, we report that the Cdc42 GTPase-activating protein (GAP) Rga6 is required for promoting monopolar growth during spore germination. The absence of Rga6 increases the number of spores that grow in a bipolar fashion. Rga6 decorates the non-growing cortical region, binds phosphatidylinositol 4,5-bisphosphate, and colocalizes with the phosphatidylinositol 4,5-bisphosphate-binding protein Opy1. Overexpression of Opy1 diminishes the cortical localization of Rga6. The characteristic localization of Rga6 on the cell cortex depends on the C-terminal PBR region of Rga6. Moreover, engineered chimera composed of the Rga6 C-terminal PBR region fused to the GAP domain of Rga3 or Rga4 are sufficient to rescue the spore growth phenotype caused by the absence of Rga6. Hence, our work establishes a paradigm in which the lipid composition of the plasma membrane directs polarized cell growth by specifying the cortical localization of a GAP protein.

Realizing asynchronous finite-time robust tracking control of switched flight vehicles by using nonfragile deep reinforcement learning.

In this study, a novel nonfragile deep reinforcement learning (DRL) method was proposed to realize the finite-time control of switched unmanned flight vehicles. Control accuracy, robustness, and intelligence were enhanced in the proposed control scheme by combining conventional robust control and DRL characteristics. In the proposed control strategy, the tracking controller consists of a dynamics-based controller and a learning-based controller. The conventional robust control approach for the nominal system was used for realizing a dynamics-based baseline tracking controller. The learning-based controller based on DRL was developed to compensate model uncertainties and enhance transient control accuracy. The multiple Lyapunov function approach and mode-dependent average dwell time approach were combined to analyze the finite-time stability of flight vehicles with asynchronous switching. The linear matrix inequalities technique was used to determine the solutions of dynamics-based controllers. Online optimization was formulated as a Markov decision process. The adaptive deep deterministic policy gradient algorithm was adopted to improve efficiency and convergence. In this algorithm, the actor-critic structure was used and adaptive hyperparameters were introduced. Unlike the conventional DRL algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. We demonstrated the effectiveness of the presented algorithm through comparative simulations.

The Cdc42 GTPase-activating protein Rga6 promotes the cortical localization of septin.

Septins are a family of filament-forming GTP-binding proteins that regulate fundamental cellular activities, such as cytokinesis and cell polarity. In general, septin filaments function as barriers and scaffolds on the cell cortex. However, little is known about the mechanism that governs the recruitment and localization of the septin complex to the cell cortex. Here, we identified the Cdc42 GTPase-activating protein Rga6 as a key protein involved in promoting the localization of the septin complex to the cell cortex in the fission yeast Schizosaccharomyces pombe. Rga6 interacts with the septin complex and partially colocalizes with the septin complex on the cell cortex. Live-cell microscopy analysis further showed septin enrichment at the cortical regions adjacent to the growing cell tip. The septin enrichment likely plays a crucial role in confining active Cdc42 to the growing cell tip. Hence, our findings support a model whereby Rga6 regulates polarized cell growth partly through promoting targeted localization of the septin complex on the cell cortex. This article has an associated First Person interview with the first author of the paper.

Signature patterns revealed by microarray analyses of mice infected with influenza virus A and Streptococcus pneumoniae.

We used cDNA microarrays to identify differentially expressed genes in mice in response to infections with influenza virus A/PR/8/34 (H1N1) and Streptococcus pneumoniae. Expression microarray analysis showed up-regulation and down-regulation of many genes involved in the defense, inflammatory response and intracellular signaling pathways including chemokine, apoptosis, MAPK, Notch, Jak-STAT, T-cell receptor and complement and coagulation cascades. We have revealed signature patterns of gene expression in mice infected with two different classes of pathogens: influenza virus A and S. pneumoniae. Quantitative real-time RT-PCR results confirmed microarray results for most of the genes tested. These studies document clear differences in gene expression profiles between mice infected with influenza virus A and S. pneumoniae. Identification of genes that are differentially expressed after respiratory infections can provide insights into the mechanisms by which the host interacts with different pathogens, useful information about stage of diseases and selection of suitable targets for early diagnosis and treatments. The advantage of this novel approach is that the detection of pathogens is based on the differences in host gene expression profiles in response to different pathogens instead of detecting pathogens directly.