Inhibitory Properties of Cinnamon Bark Oil against Postharvest Pathogen Penicillium digitatum In Vitro.

Penicillium digitatum is a major postharvest pathogen that threatens the global citrus fruit industry and causes great economic losses annually. In the present study, inhibitory properties of cinnamon bark oil (CBO) against P. digitatum in vitro were investigated. Results indicated that 0.03% CBO could efficiently inhibit the spore germination, germ tube elongation, mycelial growth, colonial expansion and conidial accumulation of P. digitatum. The results of fluorescein diacetate (FDA) and MitoTraker Orange (MTO) staining also proved the suppression effects of CBO against P. digitatum. Meanwhile, CBO could inhibit green mold rots induced by P. digitatum in citrus fruit when the working concentration of CBO exceeded 0.06%. In addition, the expressions of 12 genes critical for the growth and virulence of P. digitatum were also significantly regulated under CBO stress. Through a transcriptomic analysis, a total of 1802 common differentially expressed genes (DEGs) were detected in P. digitatum after 4 h and 8 h of CBO treatment. Most of the DEG products were associated with carbohydrate, amino acid and lipid metabolism. They directly or indirectly led to the disturbance of the membrane and the generation of reactive oxygen species (ROS). Our results may deepen the understanding of antifungal properties of CBO against P. digitatum and provide the theoretical foundation to uncover the antifungal mechanism of CBO at the molecular level.

The Identification and Comparative Analysis of Non-Coding RNAs in Spores and Mycelia of Penicillium expansum.

Penicillium expansum is the most popular post-harvest pathogen and causes blue mold disease in pome fruit and leads to significant economic losses worldwide every year. However, the fundamental regulation mechanisms of growth in P. expansum are unclear. Recently, non-coding RNAs (ncRNAs) have attracted more attention due to critical roles in normalizing gene expression and maintaining cellular genotypes in organisms. However, the research related to ncRNAs in P. expansum have not been reported. Therefore, to provide an overview of ncRNAs on composition, distribution, expression changes, and potential targets in the growth process, a comparative transcriptomic analysis was performed on spores and mycelia of P. expansum in the present study. A total of 2595 novel mRNAs, 3362 long non-coding RNAs (lncRNAs), 10 novel microRNAs (miRNAs), 86 novel small interfering RNAs (siRNAs), and 11,238 circular RNAs (circRNAs) were predicted and quantified. Of these, 1482 novel mRNAs, 5987 known mRNAs, 2047 lncRNAs, 40 miRNAs, 38 novel siRNAs, and 9235 circRNAs were differentially expressed (DE) in response to the different development stages. Afterward, the involved functions and pathways of DE RNAs were revealed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database enrichment analysis. The interaction networks between mRNAs, lncRNAs, and miRNAs were also predicted based on their correlation coefficient of expression profiles. Among them, it was found that miR168 family members may play important roles in fungal growth due to their central location in the network. These findings will contribute to a better understanding on regulation machinery at the RNA level on fungal growth and provide a theoretical basis to develop novel control strategies against P. expansum.

Proteomic Changes in Response to Colorless nonripening Mutation during Tomato Fruit Ripening.

SlSPL-CNR is a multifunctional transcription factor gene that plays important roles in regulating tomato fruit ripening. However, the molecular basis of SlSPL-CNR in the regulatory networks is not exactly clear. In the present study, the biochemical characteristics and expression levels of genes involved in ethylene biosynthesis in Colorless nonripening (Cnr) natural mutant were determined. The proteomic changes during the ripening stage were also uncovered by isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic analysis. Results indicated that both the lycopene content and soluble solid content (SSC) in Cnr fruit were lower than those in wild-type AC fruit. Meanwhile, pH, flavonoid content, and chlorophyll content were higher in Cnr fruit. Expressions of genes involved in ethylene biosynthesis were also downregulated or delayed in Cnr fruit. Furthermore, 1024 and 1234 differentially expressed proteins (DEPs) were respectively identified for the breaker and 10 days postbreaker stages. Among them, a total of 512 proteins were differentially expressed at both stages. In addition, the functions of DEPs were classified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results would lay the groundwork for wider explorations of the regulatory mechanism of SlSPL-CNR on tomato fruit ripening.

Metabolic reprogramming of myeloid-derived suppressor cells: An innovative approach confronting challenges.

Immune cells such as T cells, macrophages, dendritic cells, and other immunoregulatory cells undergo metabolic reprogramming in cancer and inflammation-derived microenvironment to meet specific physiologic and functional demands. Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells that are characterized by immunosuppressive activity, which plays a key role in host immune homeostasis. In this review, we have discussed the core metabolic pathways, including glycolysis, lipid and fatty acid biosynthesis, and amino acid metabolism in the MDSCs under various pathologic situations. Metabolic reprogramming is a determinant of the phenotype and functions of MDSCs, and is therefore a novel therapeutic possibility in various diseases.

mTOR Signaling Regulates the Development and Therapeutic Efficacy of PMN-MDSCs in Acute GVHD.

Myeloid-derived suppressor cells (MDSCs) represent a population of heterogeneous myeloid cells, which are characterized by their remarkable ability to suppress T cells and natural killer cells. MDSCs have been proven to play a positive role in protecting acute graft-versus-host disease (aGVHD). Here, we aimed to describe the mechanism behind how mTOR signaling regulates MDSCs' generation and explore its prophylactic and therapeutic potential in aGVHD. Reducing mTOR expression retains myeloid cells with immature characteristics and promotes polymorphonuclear MDSC (PMN-MDSC) immunosuppressive function through STAT3-C/EBPß pathway. Prophylactic transfusion of mTORKO PMN-MDSCs could alleviate aGVHD while maintaining the graft-versus-leukemia (GVL) effect, which could downregulate the Th1/Th2 ratio, decrease serum proinflammatory cytokines, and increase the proportion of regulatory T cells (Tregs) in aGVHD models at the early stage after transplantation. Moreover, transfusion therapy could promote the reconstruction and function of donor-derived PMN-MDSCs. Not only the percentage and the absolute number of donor-derived PMN-MDSCs significantly increased but also the immunosuppressive ability was much more robust compared to other groups. Altogether, these findings indicated that mTOR is an intrinsic regulator for PMN-MDSCs' differentiation and immunosuppressive function. Together, mTORKO PMN-MDSC transfusion can play a protective role in alleviating cytokine storm at the initial stage and promoting the quantitative and functional recoveries of donor-derived PMN-MDSCs in aGVHD.

Hyper-Laplacian regularized multi-view subspace clustering with low-rank tensor constraint.

In this paper, we propose a novel hyper-Laplacian regularized multiview subspace clustering with low-rank tensor constraint method, which is referred as HLR-MSCLRT. In the HLR-MSCLRT model, the subspace representation matrices of different views are stacked as a tensor, and then the high order correlations among data can be captured. To reduce the redundancy information of the learned subspace representations, a low-rank constraint is adopted to the constructed tensor. Since data in the real world often reside in multiple nonlinear subspaces, the HLR-MSCLRT model utilizes the hyper-Laplacian graph regularization to preserve the local geometry structure embedded in a high-dimensional ambient space. An efficient algorithm is also presented to solve the optimization problem of the HLR-MSCLRT model. The experimental results on some data sets show that the proposed HLR-MSCLRT model outperforms many state-of-the-art multi-view clustering approaches.