Showing papers by "Weiyong Liu published in 2021"
TL;DR: Wang et al. as discussed by the authors identified that SARS-CoV-2 ORF3a and host hypoxia-inducible factor-1α play key roles in the virus infection and pro-inflammatory responses.
Abstract: Cytokine storm induced by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a major pathological feature of Coronavirus Disease 2019 (COVID-19) and a crucial determinant in COVID-19 prognosis. Understanding the mechanism underlying the SARS-CoV-2-induced cytokine storm is critical for COVID-19 control. Here, we identify that SARS-CoV-2 ORF3a and host hypoxia-inducible factor-1α (HIF-1α) play key roles in the virus infection and pro-inflammatory responses. RNA sequencing shows that HIF-1α signaling, immune response, and metabolism pathways are dysregulated in COVID-19 patients. Clinical analyses indicate that HIF-1α production, inflammatory responses, and high mortalities occurr in elderly patients. HIF-1α and pro-inflammatory cytokines are elicited in patients and infected cells. Interestingly, SARS-CoV-2 ORF3a induces mitochondrial damage and Mito-ROS production to promote HIF-1α expression, which subsequently facilitates SARS-CoV-2 infection and cytokines production. Notably, HIF-1α also broadly promotes the infection of other viruses. Collectively, during SARS-CoV-2 infection, ORF3a induces HIF-1α, which in turn aggravates viral infection and inflammatory responses. Therefore, HIF-1α plays an important role in promoting SARS-CoV-2 infection and inducing pro-inflammatory responses to COVID-19.
78 citations
TL;DR: Toll-like receptors (TLRs) are essential for the protection of the host from pathogen infections by initiating the integration of contextual cues to regulate inflammation and immunity as discussed by the authors, however, the TLRs are not sufficient for all cases.
Abstract: Toll-like receptors (TLRs) are essential for the protection of the host from pathogen infections by initiating the integration of contextual cues to regulate inflammation and immunity. However, wit...
7 citations
TL;DR: Li et al. as discussed by the authors proposed a self-attention deep learning model, iEnhancer-CLA, for identifying enhancers and their strengths, which automatically learns sequence 1D features through multiscale convolutional neural networks (CNN), and employs a selfattention mechanism to represent global features formed by multiple elements.
Abstract: Enhancer is a class of non-coding DNA cis-acting elements that plays a crucial role in the development of eukaryotes for their transcription. Computational methods for predicting enhancers have been developed and achieve satisfactory performance. However, existing computational methods suffer from experience-based feature engineering and lack of interpretability, which not only limit the representation ability of the models to some extent, but also make it difficult to provide interpretable analysis of the model prediction findings.In this paper, we propose a novel deep-learning-based model, iEnhancer-CLA, for identifying enhancers and their strengths. Specifically, iEnhancer-CLA automatically learns sequence 1D features through multiscale convolutional neural networks (CNN), and employs a self-attention mechanism to represent global features formed by multiple elements (multibody effects). In particular, the model can provide an interpretable analysis of the enhancer motifs and key base signals by decoupling CNN modules and generating self-attention weights. To avoid the bias of setting hyperparameters manually, we construct Bayesian optimization methods to obtain model global optimization hyperparameters. The results demonstrate that our method outperforms existing predictors in terms of accuracy for identifying enhancers and their strengths. Importantly, our analyses found that the distribution of bases in enhancers is uneven and the base G contents are more enriched, while the distribution of bases in non-enhancers is relatively even. This result contributes to the improvement of prediction performance and thus facilitates revealing an in-depth understanding of the potential functional mechanisms of enhancers. Author summaryThe enhancers contain many subspecies and the accuracy of existing models is difficult to improve due to the small data set. Motivated by the need for accurate and efficient methods to predict enhancer types, we developed a self-attention deep learning model iEnhancer-CLA, the aim is to be able to distinguish effectively and quickly between subspecies of enhancers and whether they are enhancers or not. The model is able to learn sequence features effectively through the combination of multi-scale CNN blocks, BLSTM layers, and self-attention mechanisms, thus improving the accuracy of the model. Encouragingly, by decoupling the CNN layer it was found that the layer was effective in learning the motif of the sequences, which in combination with the self-attention weights could provide interpretability to the model. We further performed sequence analysis in conjunction with the model-generated weights and discovered differences in enhancer and non-enhancer sequence characteristics. This phenomenon can be a guide for the construction of subsequent models for identifying enhancer sequences.
1 citations
TL;DR: In this paper, anaphase-promoting complex subunit 10 (APC10), a substrate recognition protein of APC/C, was found to serve as a switch for NLRP3 inflammasome activation during cell cycle.
1 citations
TL;DR: In this paper, gallic acid, a catechol compound selected from derivatives of methyl 3,4-dihydroxybenzoate (MDHB), selectively induces NSCs to differentiate into immature neurons and promotes proliferation by activating phosphorylation of key proteins in the MAPK-ERK pathway.
Abstract: Differentiation and proliferation of neural stem cells (NSCs) are both important biological processes in cerebral neural network. However, these two capacities of NSCs are limited. Thus, the induction of differentiation and/or proliferation via administration of small molecules derived from natural plants can be considered as a potential approach to repair damaged neural networks. This paper reports that gallic acid, a catechol compound selected from derivatives of methyl 3,4-dihydroxybenzoate (MDHB), selectively induces NSCs to differentiate into immature neurons and promotes proliferation by activating phosphorylation of key proteins in the MAPK-ERK pathway. In addition, we found that 3,4-dihydroxybenzoic acid was the main active structure which could show neurotrophic activity. The substitution of carboxyl group on the benzene ring into ester group may promote differentiation on the basis of the structure of 3,4-dihydroxybenzoic acid. Meanwhile, the introduction of 5-hydroxyl group may promote proliferation. Generally, this study identified a natural catechol compound that promotes differentiation and proliferation of NSCs in vitro.