A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Discovery of Potential Anti-infective Therapy Targeting Glutamine Synthetase in Staphylococcus xylosus.

Glutamine synthetase (GS), which catalyzes the production of glutamine, plays essential roles in most biological growth and biofilm formation, suggesting that GS may be used as a promising target for antibacterial therapy. We asked whether a GS inhibitor could be found as an anti-infective agent of Staphylococcus xylosus (S. xylosus). Here, computational prediction followed by experimental testing was used to characterize GS. Sorafenib was finally determined through computational prediction. In vitro experiments showed that sorafenib has an inhibitory effect on the growth of S. xylosus by competitively occupying the active site of GS, and the minimum inhibitory concentration was 4 mg/L. In vivo experiments also proved that treatment with sorafenib significantly reduced the levels of TNF-α and IL-6 in breast tissue from mice mastitis, which was further confirmed by histopathology examination. These findings indicated that sorafenib could be utilized as an anti-infective agent for the treatment of infections caused by S. xylosus.