Abstract: The RNA-dependent RNA polymerase (RdRp) and 3C-like protease (3CLpro) from SARS-CoV-2 play crucial roles in the viral life cycle and are considered the most promising targets for drug discovery against SARS-CoV-2. In this study, FDA-approved drugs were screened to identify the probable anti-RdRp and 3CLpro inhibitors by molecular docking approach. The number of ligands selected from the PubChem database of NCBI for screening was 1760. Ligands were energy minimized using Open Babel. The RdRp and 3CLpro protein sequences were retrieved from the NCBI database. For Homology Modeling predictions, we used the Swiss model server. Their structure was then energetically minimized using SPDB viewer software and visualized in the CHIMERA UCSF software. Molecular dockings were performed using AutoDock Vina, and candidate drugs were selected based on binding affinity (G). Hydrogen bonding and hydrophobic interactions between ligands and proteins were visualized using Ligplot and the Discovery Studio Visualizer v3.0 software. Our results showed 58 drugs against RdRp, which had binding energy of - 8.5 or less, and 69 drugs to inhibit the 3CLpro enzyme with a binding energy of - 8.1 or less. Six drugs based on binding energy and number of hydrogen bonds were chosen for the next step of molecular dynamics (MD) simulations to investigate drug-protein interactions (including Nilotinib, Imatinib and dihydroergotamine for 3clpro and Lapatinib, Dexasone and Relategravir for RdRp). Except for Lapatinib, other drugs-complexes were stable during MD simulation. Raltegravir, an anti-HIV drug, was observed to be the best compound against RdRp based on docking binding energy (-9.5 kcal/mole) and MD results. According to the MD results and binding energy, dihydroergotamine is a suitable candidate for 3clpro inhibition (-9.6 kcal/mol). These drugs were classified into several categories, including antiviral, antibacterial, anti-inflammatory, anti-allergic, cardiovascular, anticoagulant, BPH and impotence, antipsychotic, antimigraine, anticancer, and so on. The common prescription-indications for some of these medication categories appeared somewhat in line with manifestations of COVID-19. We hope that they can be beneficial for patients with certain specific symptoms of SARS-CoV-2 infection, but they can also probably inhibit viral enzymes. We recommend further experimental evaluations in vitro and in vivo on these FDA-approved drugs to assess their potential antiviral effect on SARS-CoV-2.

Abstract: Recent studies have revealed that colorectal cancer (CRC) displays intratumor genetic heterogeneity, and that the cancer microenvironment plays an important role in the proliferation, invasion and metastasis of CRC. The present study performed genomic analysis on paired primary CRC and synchronous colorectal liver metastasis (CRLM) tissues collected from 22 patients using whole-exome sequencing, cancer gene panels and microarray gene expression profiling. In addition, immunohistochemical analysis was used to confirm the protein expression levels of genes identified as highly expressed in CRLM by DNA microarray analysis. The present study identified 10 genes that were highly expressed in CRLM compared with in CRC, from 36,022 probes obtained from primary CRC, CRLM and normal liver tissues by gene expression analysis with DNA microarrays. Of the 10 genes identified, five were classified as encoding 'matricellular proteins' [(osteopontin, periostin, thrombospondin-2, matrix Gla protein (MGP) and glycoprotein nonmetastatic melanoma protein B (GPNMB)] and were selected for immunohistochemical analysis. Osteopontin was strongly expressed in CRLM (6 of 22 cases: 27.3%), but not in CRC (0 of 22: 0%; P=0.02). Periostin also exhibited strong immunoreactivity in CRLM (17 of 22: 68.2%) compared with in CRC (7 of 22: 31.8%; P=0.006). Thrombospondin-2 exhibited strong immunoreactivity in both CRC and CRLM (54.5% in CRC, 45.5% in CRLM; P=0.55). GPNMB and MGP were rarely positive for both CRC and CRLM. A comparison of immunoreactive positive factors for these five genes revealed the complexities of gene expression in CRLM. Of the cases examined, 16 (72.7%) cases of CRC showed zero or only one positive immunoreactive factor. By contrast, CRLM showed more frequent and multiple immunoreactive factors; for example, 16 cases (72.7%) shared two or more factors, which was statistically more frequent than in CRC (P=0.007). The present study revealed the genomic heterogeneity between paired primary CRC and CRLM, in terms of cancer cell microenvironment. This finding may lead to novel diagnostic and therapeutic targets in the era of genome-guided personalized cancer treatment.

Abstract: Angina pectoris (AP) is the most common symptom of cardiovascular diseases, which seriously affects the quality of life in cardiovascular patients. Kuanxiong (KX) Aerosol (), a compound preparation that consists of 5 traditional Chinese medicines: Herba Asari , Rhizoma Alpiniae Officinarum, Lignum Santali Albi, Fructus Piperis Longi, and Borneolum, has been used in the treatment of AP for many years, exhibiting a significant curative effect and less side-effect. For the convenience and comprehensive understanding of KX Aerosol, this review systematically summarizes evidence on KX Aerosol in the treatment of AP including the pharmacological effects of its composition, clinical research, animal experiments, and network pharmacology prediction. Meanwhile, we highlight the research limitation of KX Aerosol at present. This review may guide the clinical application of KX Aerosol and further provide a reference for the research of AP.

Abstract: Nanoformulations of therapeutic drugs are transforming our ability to effectively deliver and treat a myriad of conditions. Often, however, they are complex to produce and exhibit low drug loading, except for nanoparticles formed via co-assembly of drugs and small molecular dyes, which display drug-loading capacities of up to 95%. There is currently no understanding of which of the millions of small-molecule combinations can result in the formation of these nanoparticles. Here we report the integration of machine learning with high-throughput experimentation to enable the rapid and large-scale identification of such nanoformulations. We identified 100 self-assembling drug nanoparticles from 2.1 million pairings, each including one of 788 candidate drugs and one of 2,686 approved excipients. We further characterized two nanoparticles, sorafenib-glycyrrhizin and terbinafine-taurocholic acid both ex vivo and in vivo. We anticipate that our platform can accelerate the development of safer and more efficacious nanoformulations with high drug-loading capacities for a wide range of therapeutics.

Abstract: In the field of drug-target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task. These methods used binary drug-target interaction datasets to train their models. The prediction of drug-target interactions is inherently a regression problem and these interactions would be identified according to the binding affinity between drugs and targets. This paper deals the binary drug-target interactions and tries to identify the binary interactions based on the binding strength of a drug and its target. To this end, we propose a semi-supervised transfer learning approach to predict the binding affinity in a continuous spectrum for binary interactions. Due to the lack of training data with continuous binding affinity in the target domain, the proposed method makes use of the information available in other domains (i.e. source domain), via the transfer learning approach. The general framework of our algorithm is based on an objective function, which considers the performance in both source and target domains as well as the unlabeled data in the target domain via a regularization term. To optimize this objective function, we make use of a gradient boosting machine which constructs the final model. To assess the performance of the proposed method, we have used some benchmark datasets with binary interactions for four classes of human proteins. Our algorithm identifies interactions in a more realistic situation. According to the experimental results, our regression model performs better than the state-of-the-art methods in some procedures.

Abstract: The global outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) necessitates the rapid development of new therapies against coronavirus disease 2019 (COVID-19) infection. Here, we present the identification of 200 approved drugs, appropriate for repurposing against COVID-19. We constructed a SARS-CoV-2-induced protein network, based on disease signatures defined by COVID-19 multiomics datasets, and cross-examined these pathways against approved drugs. This analysis identified 200 drugs predicted to target SARS-CoV-2-induced pathways, 40 of which are already in COVID-19 clinical trials, testifying to the validity of the approach. Using artificial neural network analysis, we classified these 200 drugs into nine distinct pathways, within two overarching mechanisms of action (MoAs): viral replication (126) and immune response (74). Two drugs (proguanil and sulfasalazine) implicated in viral replication were shown to inhibit replication in cell assays. This unbiased and validated analysis opens new avenues for the rapid repurposing of approved drugs into clinical trials.

Abstract: Background and Objective: HuangZhi YiShen Capsule (HZYS) is a Chinese patent herbal drug that protects kidney function in diabetic kidney disease (DKD) patients. However, the pharmacologic mechanisms of HZYS remain unclear. This study would use network pharmacology to explore the pharmacologic mechanisms of HZYS. Methods: Chemical constituents of HZYS were obtained through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and literature search. Potential targets of HZYS were identified by using the TCMSP and the SwissTarget Prediction databases. DKD-related target genes were collected by using the Online Mendelian Inheritance in Man, Therapeutic Target Database, GeneCards, DisGeNET, and Drugbank databases. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to further explore the mechanisms of HZYS in treating DKD. Molecular docking was conducted to verify the potential interactions between the prime compounds and the hub genes. Results: 179 active compounds and 620 target genes were obtained, and 571 common targets were considered potential therapeutic targets. The top 10 main active compounds of HZYS were heparin, quercetin, kaempferol, luteolin, methyl14-methylpentadecanoate, methyl (Z)-11-hexadecenoate, 17-hydroxycorticosterone, 4-pregnene-17alpha, 20beta, 21-triol-3, 11-dione, wogonin, and hydroxyecdysone. Hub signaling pathways by which HZYS treating DKD were PI3K-Akt, MAPK, AGE-RAGE in diabetic complications, TNF, and apoptosis. The top 10 target genes associated with these pathways were IL6, MAPK1, AKT1, RELA, BCL2, JUN, MAPK3, MAP2K1, CASP3, and TNF. Quercetin and Luteolin were verified to have good binding capability with the hub potential targets IL6, MAPK1, AKT1 through molecular docking. Conclusion: HZYS appeared to treat DKD by regulating the inflammatory, oxidative stress, apoptotic, and fibrosis signaling pathways. This study provided a novel perspective for further research of HZYS.

Abstract: Cyanobacteria play an important role in photobiotechnology. Yet, one of their key central metabolic pathways, the tricarboxylic acid (TCA) cycle, has a unique architecture compared to most heterotrophs and still remains largely unexploited. The conversion of 2-oxoglutarate to succinate via succinyl-CoA is absent but is by-passed by several other reactions. Overall, fluxes under photoautotrophic growth conditions through the TCA cycle are low, which has implications for the production of chemicals. In this study, we investigate the capacity of the TCA cycle of Synechocystis sp PCC 6803 for the production of trans-4-hydroxy-L-proline (Hyp), a valuable chiral building block for the pharmaceutical and cosmetic industries. For the first time, photoautotrophic Hyp production was achieved in a cyanobacterium expressing the gene for the L-proline-4-hydroxylase (P4H) from Dactylosporangium sp. strain RH1. Interestingly, while elevated intracellular Hyp concentrations could be detected in the recombinant Synechocystis strains under all tested conditions, detectable Hyp secretion into the medium was only observed when the pH of the medium exceeded 9.5 and mostly in the late phases of the cultivation. We compared the rates obtained for autotrophic Hyp production with published sugar-based production rates in E. coli. The land-use efficiency (space-time yield) of the phototrophic process is already in the same order of magnitude as the heterotrophic process considering sugar farming as well. But, the remarkable plasticity of the cyanobacterial TCA cycle promises the potential for a 23-55 fold increase in space-time yield when using Synechocystis. Altogether, these findings contribute to a better understanding of bioproduction from the TCA cycle in photoautotrophs and broaden the spectrum of chemicals produced in metabolically engineered cyanobacteria.

Abstract: Novel COVID-19 is a highly infectious disease that is caused by the recently discovered SARS-CoV-2. It is a fast-spreading disease that urgently requires therapeutics. The current study employed computational regression methods to target the ADP-ribose phosphatase (ADRP) domain of Nsp3 using FDA-approved drugs. Identified leads were further investigated using molecular dynamics simulation (MDS). The screening and MDS results suggest that trifluperidol could be a novel inhibitor of the ADRP domain of Nsp3. Trifluperidol could, therefore, be used to help control the spread of COVID-19, either alone or in combination with antiviral agents.

Abstract: This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.