CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

BATMAN-TCM 2.0: an enhanced integrative database for known and predicted interactions between traditional Chinese medicine ingredients and target proteins.

Traditional Chinese medicine (TCM) is increasingly recognized and utilized worldwide. However, the complex ingredients of TCM and their interactions with the human body make elucidating molecular mechanisms challenging, which greatly hinders the modernization of TCM. In 2016, we developed BATMAN-TCM 1.0, which is an integrated database of TCM ingredient-target protein interaction (TTI) for pharmacology research. Here, to address the growing need for a higher coverage TTI dataset, and using omics data to screen active TCM ingredients or herbs for complex disease treatment, we updated BATMAN-TCM to version 2.0 (http://bionet.ncpsb.org.cn/batman-tcm/). Using the same protocol as version 1.0, we collected 17 068 known TTIs by manual curation (with a 62.3-fold increase), and predicted ∼2.3 million high-confidence TTIs. In addition, we incorporated three new features into the updated version: (i) it enables simultaneous exploration of the target of TCM ingredient for pharmacology research and TCM ingredients binding to target proteins for drug discovery; (ii) it has significantly expanded TTI coverage; and (iii) the website was redesigned for better user experience and higher speed. We believe that BATMAN-TCM 2.0, as a discovery repository, will contribute to the study of TCM molecular mechanisms and the development of new drugs for complex diseases.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response.

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

Cladosporium cladosporioides XJ-AC03, an aconitine-producing endophytic fungus isolated from Aconitum leucostomum.

The endophytic fungus XJ-AC03, which was isolated from the healthy roots of Aconitum leucostomum, produced aconitine when grown in potato dextrose agar (PDA) medium. The presence of aconitine was confirmed by the chromatographic and spectroscopic analyses. The yield of aconitine was recorded as 236.4 µg/g by high performance liquid chromatography (HPLC). The mass spectrometry was shown to be identical to authentic aconitine. Further analysis with nuclear magnetic resonance (NMR) spectroscopy to show the chemical structure of the fungal aconitine indicated that the fungal aconitine produced an NMR spectrum identical to that of authentic aconitine. Strain XJ-AC03 was identified as Cladosporium cladosporioides by its characteristic culture morphology and ITS rDNA sequence analysis.

DBLiPro: A Database for Lipids and Proteins in Human Lipid Metabolism.

To help researchers in the field of biology, medicine, chemistry, and materials science to use lipidomic data conveniently, there is an urgent need to develop a platform that provides a systematic knowledgebase of human lipid metabolism and lipidome-centric omics analysis tools. DBLiPro is a user-friendly webserver allowing for access to human metabolism-related lipids and proteins knowledge database and an interactive bioinformatics integrative analysis workflow for lipidomics, transcriptomics, and proteomics data. In DBLiPro, there are 3109 lipid-associated proteins (LAPs) and 2098 lipid metabolites in the knowledge base section, which were obtained from Uniprot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further annotated by information from other public resources in the knowledge base section, such as RaftProt and PubChem. DBLiPro offers a step-by-step interactive analysis workflow for lipidomics, transcriptomics, proteomics, and their integrating multi-omics analysis focusing on the human lipid metabolism. In summary, DBLiPro is capable of helping users discover key molecules (lipids and proteins) in human lipid metabolism and investigate lipid-protein functions underlying mechanisms based on their own omics data. The DBLiPro is freely available at http://lipid.cloudna.cn/home.

CAD v1.0: Cancer Antigens Database Platform for Cancer Antigen Algorithm Development and Information Exploration.

Cancer vaccines have gradually attracted attention for their tremendous preclinical and clinical performance. With the development of next-generation sequencing technologies and related algorithms, pipelines based on sequencing and machine learning methods have become mainstream in cancer antigen prediction; of particular focus are neoantigens, mutation peptides that only exist in tumor cells that lack central tolerance and have fewer side effects. The rapid prediction and filtering of neoantigen peptides are crucial to the development of neoantigen-based cancer vaccines. However, due to the lack of verified neoantigen datasets and insufficient research on the properties of neoantigens, neoantigen prediction algorithms still need to be improved. Here, we recruited verified cancer antigen peptides and collected as much relevant peptide information as possible. Then, we discussed the role of each dataset for algorithm improvement in cancer antigen research, especially neoantigen prediction. A platform, Cancer Antigens Database (CAD, http://cad.bio-it.cn/), was designed to facilitate users to perform a complete exploration of cancer antigens online.

Probing structural features and binding mode of 3-arylpyrimidin-2,4-diones within housefly γ-aminobutyric acid (GABA) receptor.

In order to obtain structural features of 3-arylpyrimidin-2,4-diones emerged as promising inhibitors of insect γ-aminobutyric acid (GABA) receptor, a set of ligand-/receptor-based 3D-QSAR models for 60 derivatives are generated using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Index Analysis (CoMSIA). The statistically optimal CoMSIA model is produced with highest q(2) of 0.62, r(2) (ncv) of 0.97, and r(2) (pred) of 0.95. A minor/bulky electronegative hydrophilic polar substituent at the 1-/6-postion of the uracil ring, and bulky substituents at the 3'-, 4'- and 5'-positions of the benzene ring are beneficial for the enhanced potency of the inhibitors as revealed by the obtained 3D-contour maps. Furthermore, homology modeling, molecular dynamics (MD) simulation and molecular docking are also carried out to gain a better understanding of the probable binding modes of these inhibitors, and the results show that residues Ala-183(C), Thr-187(B), Thr-187(D) and Thr-187(E) in the second transmembrane domains of GABA receptor are responsible for the H-bonding interactions with the inhibitor. The good correlation between docking observations and 3D-QSAR analyses further proves the model reasonability in probing the structural features and the binding mode of 3-arylpyrimidin-2,4-dione derivatives within the housefly GABA receptor.

Structural determinants of imidacloprid-based nicotinic acetylcholine receptor inhibitors identified using 3D-QSAR, docking and molecular dynamics.

Nicotinic acetylcholine receptor (nAChR) is a target for insect-selective neonicotinoid insecticides (NNs), exemplified by imidacloprid (IMI). In the present study, 78 IMI derivatives reported as inhibitors of Drosophila melanogaster nAChR (Dm-nAChR) and Musca domestica nAChR (Md-nAChR) were used for three-dimensional quantitative structure-activity relationship (3D-QSAR) studies. Two optimal models with good predictive power were obtained: Q(2) = 0.64, R(2)(pred) = 0.72 for Dm-nAChR, and Q(2) = 0.63, R(2)(pred) = 0.62 for Md-nAChR. In addition, homology modeling, molecular dynamic (MD) simulation, and molecular docking also showed that amino acids located within loops A, C, D and E play key roles in the interaction of Dm-/Md-nAChR with NNs. This is highly consistent with the results of graphical analysis of 3D-QSAR contour plots. Mutation analysis also implicates the Y/S mutation within loop B as being associated closely with NN resistance in Drosophila and Musca. The results obtained lead to a better understanding not only of interactions between these antagonists and Dm-/Md-nAChR, but also of the essential features that should be considered when designing novel inhibitors with desired activities.