Target of Rapamycin Regulates Photosynthesis and Cell Growth in Auxenochlorella pyrenoidosa

Auxenochlorella pyrenoidosa is an efficient photosynthetic microalga with autotrophic growth and reproduction, which has the advantages of rich nutrition and high protein content. Target of rapamycin (TOR) is a conserved protein kinase in eukaryotes both structurally and functionally, but little is known about the TOR signalling in Auxenochlorella pyrenoidosa. Here, we found a conserved ApTOR protein in Auxenochlorella pyrenoidosa, and the key components of TOR complex 1 (TORC1) were present, while the components RICTOR and SIN1 of the TORC2 were absent in Auxenochlorella pyrenoidosa. Drug sensitivity experiments showed that AZD8055 could effectively inhibit the growth of Auxenochlorella pyrenoidosa, whereas rapamycin, Torin1 and KU0063794 had no obvious effect on the growth of Auxenochlorella pyrenoidosa a. Transcriptome data results indicated that Auxenochlorella pyrenoidosa TOR (ApTOR) regulates various intracellular metabolism and signaling pathways in Auxenochlorella pyrenoidosa. Most genes related to chloroplast development and photosynthesis were significantly down-regulated under ApTOR inhibition by AZD8055. In addition, ApTOR was involved in regulating protein synthesis and catabolism by multiple metabolic pathways in Auxenochlorella pyrenoidosa. Importantly, the inhibition of ApTOR by AZD8055 disrupted the normal carbon and nitrogen metabolism, protein and fatty acid metabolism, and TCA cycle of Auxenochlorella pyrenoidosa cells, thus inhibiting the growth of Auxenochlorella pyrenoidosa. These RNA-seq results indicated that ApTOR plays important roles in photosynthesis, intracellular metabolism and cell growth, and provided some insights into the function of ApTOR in Auxenochlorella pyrenoidosa

GANN: Graph Alignment Neural Network for Semi-Supervised Learning

Graph neural networks (GNNs) have been widely investigated in the field of semi-supervised graph machine learning. Most methods fail to exploit adequate graph information when labeled data is limited, leading to the problem of oversmoothing. To overcome this issue, we propose the Graph Alignment Neural Network (GANN), a simple and effective graph neural architecture. A unique learning algorithm with three alignment rules is proposed to thoroughly explore hidden information for insufficient labels. Firstly, to better investigate attribute specifics, we suggest the feature alignment rule to align the inner product of both the attribute and embedding matrices. Secondly, to properly utilize the higher-order neighbor information, we propose the cluster center alignment rule, which involves aligning the inner product of the cluster center matrix with the unit matrix. Finally, to get reliable prediction results with few labels, we establish the minimum entropy alignment rule by lining up the prediction probability matrix with its sharpened result. Extensive studies on graph benchmark datasets demonstrate that GANN can achieve considerable benefits in semi-supervised node classification and outperform state-of-the-art competitors

Functional Characterization of Target of Rapamycin Signaling in Verticillium dahliae

More than 200 plants have been suffering from Verticillium wilt caused by Verticillium dahliae (V. dahliae) across the world. The target of rapamycin (TOR) is a lethal gene and controls cell growth and development in various eukaryotes, but little is known about TOR signaling in V. dahliae. Here, we found that V. dahliae strain is hypersensitive to rapamycin in the presence of rapamycin binding protein VdFKBP12 while the deletion mutant aaavdfkbp12 is insensitive to rapamycin. Heterologous expressing VdFKBP12 in Arabidopsis conferred rapamycin sensitivity, indicating that VdFKBP12 can bridge the interaction between rapamycin and TOR across species. The key across species of TOR complex 1 (TORC1) and TORC2 have been identified in V. dahliae, suggesting that TOR signaling pathway is evolutionarily conserved in eukaryotic species. Furthermore, the RNA-seq analysis showed that ribosomal biogenesis, RNA polymerase II transcription factors and many metabolic processes were significantly suppressed in rapamycin treated cells of V. dahliae. Importantly, transcript levels of genes associated with cell wall degrading enzymes (CWEDs) were dramatically down-regulated in TOR-inhibited cells. Further infection assay showed that the pathogenicity of V. dahliae and occurrence of Verticillium wilt can be blocked in the presence of rapamycin. These observations suggested that VdTOR is a key target of V. dahliae for controlling and preventing Verticillium wilt in plants

Target of Rapamycin Signaling Involved in the Regulation of Photosynthesis and Cellular Metabolism in <i>Chlorella sorokiniana</i>

Target of rapamycin (TOR) is a serine/threonine protein kinase that plays a central regulating role in cell proliferation, growth, and metabolism, but little is known about the TOR signaling pathway in Chlorella sorokiniana. In this study, a Chlorella sorokiniana DP-1 strain was isolated and identified, and its nutritional compositions were analyzed. Based on homologous sequence analysis, the conserved CsTOR protein was found in the genome of Chlorella sorokiniana. In addition, the key components of TOR complex 1 (TORC1) were present, but the components of TORC2 (RICTOR and SIN1) were absent in Chlorella sorokiniana. Pharmacological assays showed that Chlorella sorokiniana DP-1 was insensitive to rapamycin, Torin1 and KU0063794, whereas AZD8055 could significantly inhibit the growth of Chlorella sorokiniana. RNA-seq analysis showed that CsTOR regulated various metabolic processes and signal transduction pathways in AZD8055-treated Chlorella sorokiniana DP-1. Most genes involved in photosynthesis and carbon fixation in Chlorella sorokiniana DP-1 were significantly downregulated under CsTOR inhibition, indicating that CsTOR positively regulated the photosynthesis in Chlorella sorokiniana. Furthermore, CsTOR controlled protein synthesis and degradation by positively regulating ribosome synthesis and negatively regulating autophagy. These observations suggested that CsTOR plays an important role in photosynthesis and cellular metabolism, and provide new insights into the function of CsTOR in Chlorella sorokiniana

Target of Rapamycin Regulates Photosynthesis and Cell Growth in Auxenochlorella pyrenoidosa

Auxenochlorella pyrenoidosa is an efficient photosynthetic microalga with autotrophic growth and reproduction, which has the advantages of rich nutrition and high protein content. Target of rapamycin (TOR) is a conserved protein kinase in eukaryotes both structurally and functionally, but little is known about the TOR signalling in Auxenochlorella pyrenoidosa. Here, we found a conserved ApTOR protein in Auxenochlorella pyrenoidosa, and the key components of TOR complex 1 (TORC1) were present, while the components RICTOR and SIN1 of the TORC2 were absent in Auxenochlorella pyrenoidosa. Drug sensitivity experiments showed that AZD8055 could effectively inhibit the growth of Auxenochlorella pyrenoidosa, whereas rapamycin, Torin1 and KU0063794 had no obvious effect on the growth of Auxenochlorella pyrenoidosaa. Transcriptome data results indicated that Auxenochlorella pyrenoidosa TOR (ApTOR) regulates various intracellular metabolism and signaling pathways in Auxenochlorella pyrenoidosa. Most genes related to chloroplast development and photosynthesis were significantly down-regulated under ApTOR inhibition by AZD8055. In addition, ApTOR was involved in regulating protein synthesis and catabolism by multiple metabolic pathways in Auxenochlorella pyrenoidosa. Importantly, the inhibition of ApTOR by AZD8055 disrupted the normal carbon and nitrogen metabolism, protein and fatty acid metabolism, and TCA cycle of Auxenochlorella pyrenoidosa cells, thus inhibiting the growth of Auxenochlorella pyrenoidosa. These RNA-seq results indicated that ApTOR plays important roles in photosynthesis, intracellular metabolism and cell growth, and provided some insights into the function of ApTOR in Auxenochlorella pyrenoidosa

Modeling Analysis of the Upper Limit Water Level Mechanism in the Upstream Reservoir of a Dam Embankment

The dam embankment (DE) is a highway structure used in the Loess Plateau to integrate the functions of a highway embankment and a dam. This paper studies the upper limit water level mechanism of the reservoir in the upstream of the DE to determine the criterion for setting upper culverts on the DE. A reservoir model is first established, and then the replenishment and loss of the reservoir water is simulated. The principle of water balance is employed to obtain water level formulas for the reservoir. Finally, an engineering example is used to verify the upper limit water level mechanism. The results show that the water level of the reservoir fluctuates near an ideal balance water level and an upper limit water level exists. Moreover, the upper limit water level has no relation to the water storage time, and is only related to the reservoir shape with big upper and small bottom, the small amount of water entering the reservoir each year, and the large water loss caused by evaporation and leakage. The upper culvert setting criterion is obtained through the upper limit water level mechanism, and it will provide important reference significance for the necessity of the DE culvert setting

Deep Graph Clustering via Dual Correlation Reduction

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github

Silver-catalyzed direct conversion of epoxides into cyclopropanes using N-triftosylhydrazones

Abstract Epoxides, as a prominent small ring O-heterocyclic and the privileged pharmacophores for medicinal chemistry, have recently represented an ideal substrate for the development of single-atom replacements. The previous O-to-C replacement strategy for epoxides to date typically requires high temperatures to achieve low yields and lacks substrate range and functional group tolerance, so achieving this oxygen-carbon exchange remains a formidable challenge. Here, we report a silver-catalyzed direct conversion of epoxides into trifluoromethylcyclopropanes in a single step using trifluoromethyl N-triftosylhydrazones as carbene precursors, thereby achieving oxygen-carbon exchange via a tandem deoxygenation/[2 + 1] cycloaddition. The reaction shows broad tolerance of functional groups, allowing routine cheletropic olefin synthesis in a strategy for the net oxygen-carbon exchange reaction. The utility of this method is further showcased with the late-stage diversification of epoxides derived from bioactive natural products and drugs. Mechanistic experiments and DFT calculations elucidate the reaction mechanism and the origin of the chemo- and stereoselectivity