Inferring how gene expression in a cell is influenced by cellular microenvironment is of great importance yet challenging. In this study, we present a single-cell RNA-sequencing data based multilayer network method (scMLnet) that models not only functional intercellular communications but also intracellular gene regulatory networks (
https://github.com/SunXQlab/scMLnet
). scMLnet was applied to a scRNA-seq dataset of COVID-19 patients to decipher the microenvironmental regulation of expression of SARS-CoV-2 receptor ACE2 that has been reported to be correlated with inflammatory cytokines and COVID-19 severity. The predicted elevation of ACE2 by extracellular cytokines EGF, IFN-γ or TNF-α were experimentally validated in human lung cells and the related signaling pathway were verified to be significantly activated during SARS-COV-2 infection. Our study provided a new approach to uncover inter-/intra-cellular signaling mechanisms of gene expression and revealed microenvironmental regulators of ACE2 expression, which may facilitate designing anti-cytokine therapies or targeted therapies for controlling COVID-19 infection. In addition, we summarized and compared different methods of scRNA-seq based inter-/intra-cellular signaling network inference for facilitating new methodology development and applications.
Genomic alteration can reshape tumor microenvironment to drive tumor malignancy. However, how
PTEN
deficiency influences microenvironment-mediated cell-cell interactions in glioblastoma (GBM) remains unclear. Here, we show that
PTEN
deficiency induces a symbiotic glioma-M2 macrophage interaction to support glioma progression. Mechanistically,
PTEN
-deficient GBM cells secrete high levels of galectin-9 (Gal-9) via the AKT-GSK3β-IRF1 pathway. The secreted Gal-9 drives macrophage M2 polarization by activating its receptor Tim-3 and downstream pathways in macrophages. These macrophages, in turn, secrete VEGFA to stimulate angiogenesis and support glioma growth. Furthermore, enhanced Gal-9/Tim-3 expression predicts poor outcome in glioma patients. In GBM models, blockade of Gal-9/Tim-3 signaling inhibits macrophage M2 polarization and suppresses tumor growth. Moreover, α-lactose attenuates glioma angiogenesis by down-regulating macrophage-derived VEGFA, providing a novel antivascularization strategy. Therefore, our study suggests that blockade of Gal-9/Tim-3 signaling is effective to impair glioma progression by inhibiting macrophage M2 polarization, specifically for
PTEN
-null GBM.
Deep eutectic solvents (DESs) have been widely used to capture CO2 in recent years. Understanding CO2 mechanisms by DESs is crucial to the design of efficient DESs for carbon capture. In this work, we studied the CO2 absorption mechanism by DESs based on ethylene glycol (EG) and protic ionic liquid ([MEAH][Im]), formed by monoethanolamine (MEA) with imidazole (Im). The interactions between CO2 and DESs [MEAH][Im]-EG (1:3) are investigated thoroughly by applying 1H and 13 C nuclear magnetic resonance (NMR), 2-D NMR, and Fourier-transform infrared (FTIR) techniques. Surprisingly, the results indicate that CO2 not only binds to the amine group of MEA but also reacts with the deprotonated EG, yielding carbamate and carbonate species, respectively. The reaction mechanism between CO2 and DESs is proposed, which includes two pathways. One pathway is the deprotonation of the [MEAH]+ cation by the [Im]− anion, resulting in the formation of neutral molecule MEA, which then reacts with CO2 to form a carbamate species. In the other pathway, EG is deprotonated by the [Im]−, and then the deprotonated EG, HO-CH2-CH2-O−, binds with CO2 to form a carbonate species. The absorption mechanism found by this work is different from those of other DESs formed by protic ionic liquids and EG, and we believe the new insights into the interactions between CO2 and DESs will be beneficial to the design and applications of DESs for carbon capture in the future.
This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills.
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