D-allose Inhibits TLR4/PI3K/AKT Signaling to Attenuate Neuroinflammation and Neuronal Apoptosis by Inhibiting Gal-3 Following Ischemic Stroke.

BACKGROUND: Ischemic stroke (IS) occurs when a blood vessel supplying the brain becomes obstructed, resulting in cerebral ischemia. This type of stroke accounts for approximately 87% of all strokes. Globally, IS leads to high mortality and poor prognosis and is associated with neuroinflammation and neuronal apoptosis. D-allose is a bio-substrate of glucose that is widely expressed in many plants. Our previous study showed that D-allose exerted neuroprotective effects against acute cerebral ischemic/reperfusion (I/R) injury by reducing neuroinflammation. Here, we aimed to clarify the beneficial effects D-allose in suppressing IS-induced neuroinflammation damage, cytotoxicity, neuronal apoptosis and neurological deficits and the underlying mechanism in vitro and in vivo. METHODS: In vivo, an I/R model was induced by middle cerebral artery occlusion and reperfusion (MCAO/R) in C57BL/6 N mice, and D-allose was given by intraperitoneal injection within 5 min after reperfusion. In vitro, mouse hippocampal neuronal cells (HT-22) with oxygen-glucose deprivation and reperfusion (OGD/R) were established as a cell model of IS. Neurological scores, some cytokines, cytotoxicity and apoptosis in the brain and cell lines were measured. Moreover, Gal-3 short hairpin RNAs, lentiviruses and adeno-associated viruses were used to modulate Gal-3 expression in neurons in vitro and in vivo to reveal the molecular mechanism. RESULTS: D-allose alleviated cytotoxicity, including cell viability, LDH release and apoptosis, in HT-22 cells after OGD/R, which also alleviated brain injury, as indicated by lesion volume, brain edema, neuronal apoptosis, and neurological functional deficits, in a mouse model of I/R. Moreover, D-allose decreased the release of inflammatory factors, such as IL-1ß, IL-6 and TNF-α. Furthermore, the expression of Gal-3 was increased by I/R in wild-type mice and HT-22 cells, and this factor further bound to TLR4, as confirmed by three-dimensional structure prediction and Co-IP. Silencing the Gal-3 gene with shRNAs decreased the activation of TLR4 signaling and alleviated IS-induced neuroinflammation, apoptosis and brain injury. Importantly, the loss of Gal-3 enhanced the D-allose-mediated protection against I/R-induced HT-22 cell injury, inflammatory insults and apoptosis, whereas activation of TLR4 by the selective agonist LPS increased the degree of neuronal injury and abolished the protective effects of D-allose. CONCLUSIONS: In summary, D-allose plays a crucial role in inhibiting inflammation after IS by suppressing Gal-3/TLR4/PI3K/AKT signaling pathway in vitro and in vivo.

MPTN: A message-passing transformer network for drug repurposing from knowledge graph.

Drug repurposing (DR) based on knowledge graphs (KGs) is challenging, which uses knowledge graph reasoning models to predict new therapeutic pathways for existing drugs. With the rapid development of computing technology and the growing availability of validated biomedical data, various knowledge graph-based methods have been widely used to analyze and process complex and novel data to discover new indications for given drugs. However, existing methods need to be improved in extracting semantic information from contextual triples of biomedical entities. In this study, we propose a message-passing transformer network named MPTN based on knowledge graph for drug repurposing. Firstly, CompGCN is used as precoder to jointly aggregate entity and relation embeddings. Then, to fully capture the semantic information of entity context triples, the message propagating transformer module is designed. The module integrates the transformer into the message passing mechanism and incorporates the attention weight information of computing entity context triples into the entity embedding to update the entity embedding. Next, the residual connection is introduced to retain information as much as possible and improve prediction accuracy. Finally, MPTN utilizes the InteractE module as the decoder to obtain heterogeneous feature interactions in entity and relation representations and predict new pathways for drug treatment. Experiments on two datasets show that the model is superior to the existing knowledge graph embedding (KGE) learning methods.

TransFOL: A Logical Query Model for Complex Relational Reasoning in Drug-Drug Interaction.

Predicting drug-drug interaction (DDI) plays a crucial role in drug recommendation and discovery. However, wet lab methods are prohibitively expensive and time-consuming due to drug interactions. In recent years, deep learning methods have gained widespread use in drug reasoning. Although these methods have demonstrated effectiveness, they can only predict the interaction between a drug pair and do not contain any other information. However, DDI is greatly affected by various other biomedical factors (such as the dose of the drug). As a result, it is challenging to apply them to more complex and meaningful reasoning tasks. Therefore, this study regards DDI as a link prediction problem on knowledge graphs and proposes a DDI prediction model based on Cross-Transformer and Graph Convolutional Networks (GCNs) in first-order logical query form, TransFOL. In the model, a biomedical query graph is first built to learn the embedding representation. Subsequently, an enhancement module is designed to aggregate the semantics of entities and relations. Cross-Transformer is used for encoding to obtain semantic information between nodes, and GCN is used to gather neighbour information further and predict inference results. To evaluate the performance of TransFOL on common DDI tasks, we conduct experiments on two benchmark datasets. The experimental results indicate that our model outperforms state-of-the-art methods on traditional DDI tasks. Additionally, we introduce different biomedical information in the other two experiments to make the settings more realistic. Experimental results verify the strong drug reasoning ability and generalization of TransFOL in complex settings.

Construction of ecological security pattern in Ningbo based on remote sensing ecological index and graph theory knowledge.

Constructing ecological security pattern and identifying ecological important areas are the focus of current research on regional ecological security. With Ningbo City as a case study area, we identified ecological sources by remote sensing ecological index, the ecological corridors and pinch point by circuit theory model, and the minimum spanning tree and cuts by graph theory algorithm. The results showed that there were 203 ecological sources in Ningbo, and that the main type of land cover was forest, including a small amount of paddy fields and flooded vegetation. There were 368 ecological corridors with a total length of 573.42 km, being dense in the southwest and sparse in the northeast. There were 91 ecological pinch points, which mainly distributed between coastal areas and closely related ecological sources. According to current situation, we put forward the optimization strategy with 187 primary corridors, 181 secondary corridors, 50 ecological restoration priority areas and 59 long-term ecological restoration areas. The optimization strategy combined with graph theory and circuit theory model would provide a refe-rence for the constructing of ecological security pattern.

[Application of oligo-microarray in an in vitro study of the effects of pulsatile fluid shear stress on gene expression of human smooth muscle cells].

We assessed the effects of pulsatile flow shear stress on the gene expression profiles of human umbilical artery smooth muscle cells (HUASMCs) in vitro using the Express Chip DNA microarray method and investigated the difference between pulsatile and steady shear stress on differentially expressed genes of HUASMCs. In a modified pulsatile flow chamber system, HUASMCs were exposed to pulsatile and steady fluid shear stress (5.52 dyne/cm2) for 6 h respectively, and normal static cultured HUASMCs were selected as a control. The total cellular RNA was extracted by TRIzol Reagent (Life Technologies, Inc) according to the manufacturer's manual. Conversion of mRNA to single strand cDNA and double strand cDNA template was synthesized by Reverse Transcription from the total RNA. cRNA probe was transcribed with biotin labeling. After hybridization of probe with microarray, the binding of streptavidin to biotin was performed and amplified with the first antibody and further amplified with Cy3-conjugated second antibody. Then detection of Cy3 dye was carried out with ScanArray 5000. The results showed that a total of 1,330 genes revealed differential expression in HUASMCs exposed on pulsatile shear stress (5.52 dyne/cm2, 6 h); however, 2,676 genes revealed differential expression in HUASMCs exposed on steady shear stress. Comparsion of HUASMCs exposed to pulsatile with the HUASMCs exposed to steady shear stress showed there were 2,297 genes revealing differential expression. The transcriptional profile of fluidally induced genes in HUASMCs suggested a different response to pulsatile and steady shear stress.