Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications.

MOTIVATION: The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative. RESULT: In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2.

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells.

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics. This framework takes into account the topological relationship between cells and the variety of gene expression to iteratively search the global optimal solution, thereby learning the Pareto optimal cell-cell affinity matrix. Finally, we use the learned sparse relationship model between cells to improve data quality and reduce data noise. In simulated datasets, scESI performed significantly better than benchmark methods with various metrics. By applying scESI to real scRNA-seq datasets, we discovered scESI can not only further classify the cell types and separate cells in visualization successfully but also improve the performance in reconstructing trajectories differentiation and identifying differentially expressed genes. In addition, scESI successfully recovered the expression trends of marker genes in stem cell differentiation and can discover new cell types and putative pathways regulating biological processes.

Nanocrystal-Loaded Micelles for the Enhanced In Vivo Circulation of Docetaxel.

Prolonging in vivo circulation has proved to be an efficient route for enhancing the therapeutic effect of rapidly metabolized drugs. In this study, we aimed to construct a nanocrystal-loaded micelles delivery system to enhance the blood circulation of docetaxel (DOC). We employed high-pressure homogenization to prepare docetaxel nanocrystals (DOC(Nc)), and then produced docetaxel nanocrystal-loaded micelles (DOC(Nc)@mPEG-PLA) by a thin-film hydration method. The particle sizes of optimized DOC(Nc), docetaxel micelles (DOC@mPEG-PLA), and DOC(Nc)@mPEG-PLA were 168.4, 36.3, and 72.5 nm, respectively. The crystallinity of docetaxel was decreased after transforming it into nanocrystals, and the crystalline state of docetaxel in micelles was amorphous. The constructed DOC(Nc)@mPEG-PLA showed good stability as its particle size showed no significant change in 7 days. Despite their rapid dissolution, docetaxel nanocrystals exhibited higher bioavailability. The micelles prolonged the retention time of docetaxel in the circulation system of rats, and DOC(Nc)@mPEG-PLA exhibited the highest retention time and bioavailability. These results reveal that constructing nanocrystal-loaded micelles may be a promising way to enhance the in vivo circulation and bioavailability of rapidly metabolized drugs such as docetaxel.

Improving Oral Bioavailability of Luteolin Nanocrystals by Surface Modification of Sodium Dodecyl Sulfate.

Luteolin suffers from drawbacks like low solubility and bioavailability, thus hindering its application in the clinic. In this study, we employed sodium dodecyl sulfate (SDS), an efficient tight junction opening agent, to modify the surface of luteolin nanocrystals, aiming to enhance the bioavailability of luteolin (LUT) and luteolin nanocrystals (LNC). The particle sizes of SDS-modified luteolin nanocrystals (SLNC) were slightly larger than that of LNC, and the zeta potential of LNC and SLNC was -25.0 ± 0.7 mV and -43.5 ± 0.4 mV, respectively. Both LNC and SLNC exhibited enhanced saturation solubility and high stability in the liquid state. In the cellular study, we found that SDS has cytotoxicity on caco-2 cells and could open the tight junction of the caco-2 monolayer, which could lead to an enhanced transport of luteolin across the intestinal membrane. The bioavailability of luteolin was enhanced for 1.90-fold by luteolin nanocrystals, and after modification with SDS, the bioavailability was enhanced to 3.48-fold. Our experiments demonstrated that SDS could efficiently open the tight junction and enhance the bioavailability of luteolin thereafter, revealing the construction of SDS-modified nanocrystals is a good strategy for enhancing the oral bioavailability of poorly soluble drugs like luteolin.

A Clustering Ensemble Method for Cell Type Detection by Multiobjective Particle Optimization.

Single-cell RNA sequencing (scRNA-seq) is a new technology different from previous sequencing methods that measure the average expression level for each gene across a large population of cells. Thus, new computational methods are required to reveal cell types among cell populations. We present a clustering ensemble algorithm using optimized multiobjective particle (CEMP). It is featured with several mechanisms: 1) A multi-subspace projection method for mapping the original data to low-dimensional subspaces is applied in order to detect complex data structure at both gene level and sample level. 2) The basic partition module in different subspaces is utilized to generate clustering solutions. 3) A transforming representation between clusters and particles is used to bridge the gap between the discrete clustering ensemble optimization problem and the continuous multiobjective optimization algorithm. 4) We propose a clustering ensemble optimization. To guide the multiobjective ensemble optimization process, three cluster metrics are embedded into CEMP as objective functions in which the final clustering will be dynamically evaluated. Experiments on 9 real scRNA-seq datasets indicated that CEMP had superior performance over several other clustering algorithms in clustering accuracy and robustness. The case study conducted on mouse neuronal cells identified main cell types and cell subtypes successfully.

scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome.

Rapid advances in single-cell transcriptome analysis provide deeper insights into the study of tissue heterogeneity at the cellular level. Unsupervised clustering can identify potential cell populations in single-cell RNA-sequencing (scRNA-seq) data, but fail to further determine the identity of each cell. Existing automatic annotation methods using scRNA-seq data based on machine learning mainly use single feature set and single classifier. In view of this, we propose a Weighted Ensemble classification framework for Cell Type Annotation, named scWECTA, which improves the accuracy of cell type identification. scWECTA uses five informative gene sets and integrates five classifiers based on soft weighted ensemble framework. And the ensemble weights are inferred through the constrained non-negative least squares. Validated on multiple pairs of scRNA-seq datasets, scWECTA is able to accurately annotate scRNA-seq data across platforms and across tissues, especially for imbalanced data containing rare cell types. Moreover, scWECTA outperforms other comparable methods in balancing the prediction accuracy of common cell types and the unassigned rate of non-common cell types at the same time. The source code of scWECTA is freely available at https://github.com/ttren-sc/scWECTA.

MTGDC: A Multi-Scale Tensor Graph Diffusion Clustering for Single-Cell RNA Sequencing Data.

Single-cell RNA sequencing (scRNA-seq) is a new technology that focuses on the expression levels for each cell to study cell heterogeneity. Thus, new computational methods matching scRNA-seq are designed to detect cell types among various cell groups. Herein, we propose a Multi-scale Tensor Graph Diffusion Clustering (MTGDC) for single-cell RNA sequencing data. It has the following mechanisms: 1) To mine potential similarity distributions among cells, we design a multi-scale affinity learning method to construct a fully connected graph between cells; 2) For each affinity matrix, we propose an efficient tensor graph diffusion learning framework to learn high-order information among multi-scale affinity matrices. First, the tensor graph is explicitly introduced to measure cell-cell edges with local high-order relationship information. To further preserve more global topology structure information in the tensor graph, MTGDC implicitly considers the propagation of information via a data diffusion process by designing a simple and efficient tensor graph diffusion update algorithm. 3) Finally, we mix together the multi-scale tensor graphs to obtain the fusion high-order affinity matrix and apply it to spectral clustering. Experiments and case studies showed that MTGDC had obvious advantages over the state-of-art algorithms in robustness, accuracy, visualization, and speed.

LFSC: A linear fast semi-supervised clustering algorithm that integrates reference-bulk and single-cell transcriptomes.

The identification of cell types in complex tissues is an important step in research into cellular heterogeneity in disease. We present a linear fast semi-supervised clustering (LFSC) algorithm that utilizes reference samples generated from bulk RNA sequencing data to identify cell types from single-cell transcriptomes. An anchor graph is constructed to depict the relationship between reference samples and cells. By applying a connectivity constraint to the learned graph, LFSC enables the preservation of the underlying cluster structure. Moreover, the overall complexity of LFSC is linear to the size of the data, which greatly improves effectiveness and efficiency. By applying LFSC to real single-cell RNA sequencing datasets, we discovered that it has superior performance over existing baseline methods in clustering accuracy and robustness. An application using infiltrating T cells in liver cancer demonstrates that LFSC can successfully find new cell types, discover differently expressed genes, and explore new cancer-associated biomarkers.

A 13-Immune Gene Set Signature for Prediction of Colon Cancer Prognosis.

BACKGROUND: Colon cancer is one of the most common cancers worldwide and has a poor prognosis. Through the analysis of transcriptome and clinical data of colon cancer, an immune gene-set signature was identified by single sample enrichment analysis (ssGSEA) scoring to predict patient survival and discover new therapeutic targets. OBJECTIVE: To study the role of immune gene-set signature in colon cancer. METHODS: First, RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). Immune gene-related gene sets were collected from the ImmPort database. Genes and immunological pathways related to prognosis were screened in the training set and integrated for feature selection using random forest. The immune gene-related prognosis model was verified in the entire TCGA test set and GEO validation set and compared with immune cells scores and matrix score. RESULTS: A total of 1650 prognostic genes and 13 immunological pathways were identified. These genes and pathways are closely related to the development of tumors. 13-immune gene-set signature was established, which is an independent prognostic factor for patients with colon cancer. Risk stratification of samples could be carried out in the training set, test set, and external validation set. The AUC of five-year survival in the training set and validation set is greater than 0.6. Immunosuppression occurs in high-risk samples and compared with published models, riskScore has a better prediction effect. CONCLUSION: This study constructed a 13-immune gene-set signature as a new prognostic marker to predict the survival of patients with colon cancer, and provided new diagnostic/prognostic biomarkers and therapeutic targets for colon cancer.

Enhancing Oral Bioavailability by Paclitaxel Polymeric Micelles: Role of Transmembrane Pathways in the Oral Absorption.

The oral administration of paclitaxel suffering obstacles like poorly solubility and low permeability, causing extremely low oral bioavailability. In this study, we aim to study the role of transmembrane pathway on the absorption mechanisms of PTX-Micelles. In addition, we will further study the permeability of PTX-Micelles by employing in situ perfusion model. The results showed the PTX-Micelles could be up taken by caco-2 intact. In addition, we found that the endocytosis pathway and M cells pathway attended, while paracellular pathway absented in the transport process of PTX-Micelles. The study on in situ perfusion showed the absorption rate constant (Ka) of PTX was enhanced from 6.3, 6.1, 10.6 and 13.6 × 10-2/min to 15.1, 18.4, 45.6 and 42.9 × 10-2/min by micelles in duodenum, jejunum, ileum and colon, respectively, indicating the PTX-Micelles could enhance the permeability of PTX in intestinal membrane. This study indicating that transmembrane pathway-mediated transport is an important part in the transport process of polymeric micelles, and designing formulation based on transmembrane pathways may be a promising route for oral bioavailability enhancement of poor-soluble drugs.

Huanglian Jiedu Decoction ameliorates DSS-induced colitis in mice via the JAK2/STAT3 signalling pathway.

BACKGROUND: Ulcerative colitis (UC) is an intestinal disease which was characterized by intestinal inflammation, mucosal injury and fibrosis. In this paper, the effect of Huanglian Jiedu Decoction (HJD), a well-known traditional Chinese medicine with significant anti-inflammatory effect, on dextran sulphate sodium (DSS)-induced UC in mice and inhibition of JAK2/STAT3 pathway were investigated. METHODS: BALB/c mice were randomly divided into 6 groups: HJD group (high, medium and low dose), USAN group, UC group, and control group. UC in mice were induced through free access to 3% DSS solution. After being treated with HJD for 8 days, all animals were sacrifice. Pathological examination of colonic specimen was performed by H&E staining. Cytokines (TNF-α, IL-6, and IL-1ß) in colon were assayed by ELISA and immunofluorescence, MPO in colon and ATT in serum were detected by ELISA. Moreover, mice in HJD group and UC group were treated with AG490 to inhibit the expression of JAK2 protein, then the expression of JAK2 and STAT3 protein in colon was determined by western blotting and immunofluorescence staining. Furthermore, KI67 in colon was examined by immunohistochemistry, and apoptosis was detected by TUNEL staining, and collagen deposition was assayed by Masson staining after JAK2/STAT3 pathway in UC mice was inhibited by HJD. RESULTS: After mice being treated with HJD, the symptoms (weight loss and haematochezia) of UC were alleviated, and the contents of inflammatory cytokines (TNF-α, IL-6 and IL-1ß) and MPO in colon were significantly decreased. The expression of JAK2 and STAT3 protein was reduced after administration with HJD. After JAK2/STAT3 pathway being inhibited with HJD, the cell apoptosis, collagen deposition and immunoreactivity of macrophage in colon were significantly reduced, but the expression of Ki67 was markedly enhanced in both UC group and HJD group compare with control group. CONCLUSIONS: HJD treatment can alleviate intestinal mucosal damage and has the protective effect on UC by downregulating JAK2 and STAT3 expression to reduce inflammation via JAK2/STAT3 pathway.