ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation.

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor ß (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge.

The high-throughput genomic and proteomic scanning approaches allow investigators to measure the quantification of genome-wide genes (or gene products) for certain disease conditions, which plays an essential role in promoting the discovery of disease mechanisms. The high-throughput approaches often generate a large gene list of interest (GOIs), such as differentially expressed genes/proteins. However, researchers have to perform manual triage and validation to explore the most promising, biologically plausible linkages between the known disease genes and GOIs (disease signals) for further study. Here, to address this challenge, we proposed a network-based strategy DDK-Linker to facilitate the exploration of disease signals hidden in omics data by linking GOIs to disease knowns genes. Specifically, it reconstructed gene distances in the protein-protein interaction (PPI) network through six network methods (random walk with restart, Deepwalk, Node2Vec, LINE, HOPE, Laplacian) to discover disease signals in omics data that have shorter distances to disease genes. Furthermore, benefiting from the establishment of knowledge base we established, the abundant bioinformatics annotations were provided for each candidate disease signal. To assist in omics data interpretation and facilitate the usage, we have developed this strategy into an application that users can access through a website or download the R package. We believe DDK-Linker will accelerate the exploring of disease genes and drug targets in a variety of omics data, such as genomics, transcriptomics and proteomics data, and provide clues for complex disease mechanism and pharmacological research. DDK-Linker is freely accessible at http://ddklinker.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.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Human Autoantigen Atlas: Searching for the Hallmarks of Autoantigens.

Understanding autoimmunity to endogenous proteins is crucial in diagnosing and treating autoimmune diseases. In this work, we developed a user-friendly AAgAtlas portal (http://biokb.ncpsb.org.cn/aagatlas_portal/index.php#), which can be used to search for 8045 non-redundant autoantigens (AAgs) and 47 post-translationally modified AAgs against 1073 human diseases that are prioritized by a credential score developed by multisource evidence. Using AAgAtlas, the immunogenic properties of human AAgs was systematically elucidated according to their genetic, biophysical, cytological, expression profile, and evolutionary characteristics. The results indicated that human AAgs are evolutionally conserved in protein sequence and enriched in three hydrophilic and polar amino acid residues (K, D, and E) that are located at the protein surface. AAgs are enriched in proteins that are involved in nucleic acid binding, transferase, and the cytoskeleton. Genome, transcriptome, and proteome analyses further indicated that AAb production is associated with gene variance and abnormal protein expression related to the pathological activities of different tumors. Collectively, our data outlines the hallmarks of human AAgs that facilitate the understanding of humoral autoimmunity and the identification of biomarkers of human diseases.

NAD+ biosynthesis metabolism predicts prognosis and indicates immune microenvironment for breast cancer.

The growing evidence implies that tumor cells need to increase NAD+ levels by upregulating NAD+ biosynthesis to satisfy their growth demand. NAD+ biosynthesis metabolism is implicated in tumor progression. Breast cancer (BC) is the most common malignant malignancy in the world. Nevertheless, the prognostic significance of NAD+ biosynthesis and its relationship with the tumor immune microenvironment in breast cancer still need further investigation. In this study, we obtained the mRNA expression data and clinical information of BC samples from public databases and calculated the level of NAD+ biosynthesis activity by single-sample gene set enrichment analysis (ssGSEA). We then explored the relationship between the NAD+ biosynthesis score, infiltrating immune cells, prognosis significance, immunogenicity and immune checkpoint molecules. The results demonstrated that patients with high NAD+ biosynthetic score displayed poor prognosis, high immune infiltration, high immunogenicity, elevated PD-L1 expression, and might more benefit from immunotherapy. Taken together, our studies not only deepened the understanding of NAD+ biosynthesis metabolism of breast cancer but also provided new insights into personalized treatment strategies and immunological therapies to improve the outcomes of breast cancer patients.

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.

UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase-substrate interactions in eukaryotic species.

As an important post-translational modification, ubiquitination mediates ∼80% of protein degradation in eukaryotes. The degree of protein ubiquitination is tightly determined by the delicate balance between specific ubiquitin ligase (E3)-mediated ubiquitination and deubiquitinase-mediated deubiquitination. In 2017, we developed UbiBrowser 1.0, which is an integrated database for predicted human proteome-wide E3-substrate interactions. Here, to meet the urgent requirement of proteome-wide E3/deubiquitinase-substrate interactions (ESIs/DSIs) in multiple organisms, we updated UbiBrowser to version 2.0 (http://ubibrowser.ncpsb.org.cn). Using an improved protocol, we collected 4068/967 known ESIs/DSIs by manual curation, and we predicted about 2.2 million highly confident ESIs/DSIs in 39 organisms, with >210-fold increase in total data volume. In addition, we made several new features in the updated version: (i) it allows exploring proteins' upstream E3 ligases and deubiquitinases simultaneously; (ii) it has significantly increased species coverage; (iii) it presents a uniform confidence scoring system to rank predicted ESIs/DSIs. To facilitate the usage of UbiBrowser 2.0, we also redesigned the web interface for exploring these known and predicted ESIs/DSIs, and added functions of 'Browse', 'Download' and 'Application Programming Interface'. We believe that UbiBrowser 2.0, as a discovery tool, will contribute to the study of protein ubiquitination and the development of drug targets for complex diseases.

RadAtlas 1.0: a knowledgebase focusing on radiation-associated genes.

Purpose: Ionizing radiation has very complex biological effects, such as inducing damage to DNA and proteins, ionizing water molecules to produce toxic free radicals, and triggering genetic and somatic effects. Understanding the biomolecular response mechanism of radiation is very important for the prevention and treatment of radiation diseases. However, function information of these radiation-associated genes is hidden in numbers of scientific papers and databases, making it difficult to understand the response mechanism of ionizing radiation.Materials and methods: We collected radiation-associated genes by literature and database mining. Literature and database mining was performed on the basis of biomedical literature from PubMed and gene expression datasets from GEO respectively.Results: We built an ionizing radiation related knowledgebase RadAtlas 1.0 (http://biokb.ncpsb.org/radatlas), which contains 598 radiation-associated genes compiled from literature mining, and 611 potential radiation-associated genes collected from gene expression datasets by differential gene expression analysis. We also provide a user-friendly web interface that offers multiple search methods.Conclusions: RadAtlas collected a large amount of information about genes, biological processes, and pathways related to ionizing radiation. It is the first attempt to provide a comprehensive catalog of radiation-associated genes with literature evidence and potential radiation-associated genes with differential expression evidence. We believe that RadAtlas would be a helpful tool to understand the response mechanism to ionizing radiation.

Rivaroxaban in Peripheral Artery Disease after Revascularization.

BACKGROUND: Patients with peripheral artery disease who have undergone lower-extremity revascularization are at high risk for major adverse limb and cardiovascular events. The efficacy and safety of rivaroxaban in this context are uncertain. METHODS: In a double-blind trial, patients with peripheral artery disease who had undergone revascularization were randomly assigned to receive rivaroxaban (2.5 mg twice daily) plus aspirin or placebo plus aspirin. The primary efficacy outcome was a composite of acute limb ischemia, major amputation for vascular causes, myocardial infarction, ischemic stroke, or death from cardiovascular causes. The principal safety outcome was major bleeding, defined according to the Thrombolysis in Myocardial Infarction (TIMI) classification; major bleeding as defined by the International Society on Thrombosis and Haemostasis (ISTH) was a secondary safety outcome. RESULTS: A total of 6564 patients underwent randomization; 3286 were assigned to the rivaroxaban group, and 3278 were assigned to the placebo group. The primary efficacy outcome occurred in 508 patients in the rivaroxaban group and in 584 in the placebo group; the Kaplan-Meier estimates of the incidence at 3 years were 17.3% and 19.9%, respectively (hazard ratio, 0.85, 95% confidence interval [CI], 0.76 to 0.96; P = 0.009). TIMI major bleeding occurred in 62 patients in the rivaroxaban group and in 44 patients in the placebo group (2.65% and 1.87%; hazard ratio, 1.43; 95% CI, 0.97 to 2.10; P = 0.07). ISTH major bleeding occurred in 140 patients in the rivaroxaban group, as compared with 100 patients in the placebo group (5.94% and 4.06%; hazard ratio, 1.42; 95% CI, 1.10 to 1.84; P = 0.007). CONCLUSIONS: In patients with peripheral artery disease who had undergone lower-extremity revascularization, rivaroxaban at a dose of 2.5 mg twice daily plus aspirin was associated with a significantly lower incidence of the composite outcome of acute limb ischemia, major amputation for vascular causes, myocardial infarction, ischemic stroke, or death from cardiovascular causes than aspirin alone. The incidence of TIMI major bleeding did not differ significantly between the groups. The incidence of ISTH major bleeding was significantly higher with rivaroxaban and aspirin than with aspirin alone. (Funded by Bayer and Janssen Pharmaceuticals; VOYAGER PAD ClinicalTrials.gov number, NCT02504216.).

OsteoporosAtlas: a human osteoporosis-related gene database.

BACKGROUND: Osteoporosis is a common, complex disease of bone with a strong heritable component, characterized by low bone mineral density, microarchitectural deterioration of bone tissue and an increased risk of fracture. Due to limited drug selection for osteoporosis and increasing morbidity, mortality of osteoporotic fractures, osteoporosis has become a major health burden in aging societies. Current researches for identifying specific loci or genes involved in osteoporosis contribute to a greater understanding of the pathogenesis of osteoporosis and the development of better diagnosis, prevention and treatment strategies. However, little is known about how most causal genes work and interact to influence osteoporosis. Therefore, it is greatly significant to collect and analyze the studies involved in osteoporosis-related genes. Unfortunately, the information about all these osteoporosis-related genes is scattered in a large amount of extensive literature. Currently, there is no specialized database for easily accessing relevant information about osteoporosis-related genes and miRNAs. METHODS: We extracted data from literature abstracts in PubMed by text-mining and manual curation. Moreover, a local MySQL database containing all the data was developed with PHP on a Windows server. RESULTS: OsteoporosAtlas (http://biokb.ncpsb.org/osteoporosis/), the first specialized database for easily accessing relevant information such as osteoporosis-related genes and miRNAs, was constructed and served for researchers. OsteoporosAtlas enables users to retrieve, browse and download osteoporosis-related genes and miRNAs. Gene ontology and pathway analyses were integrated into OsteoporosAtlas. It currently includes 617 human encoding genes, 131 human non-coding miRNAs, and 128 functional roles. We think that OsteoporosAtlas will be an important bioinformatics resource to facilitate a better understanding of the pathogenesis of osteoporosis and developing better diagnosis, prevention and treatment strategies.

UVGD 1.0: a gene-centric database bridging ultraviolet radiation and molecular biology effects in organisms.

Objectives: Exposing to ultraviolet for a certain time will trigger some significant molecular biology effects in an organism. In the past few decades, varied ultraviolet-associated biological effects as well as their related genes, have been discovered under biologists' efforts. However, information about ultraviolet-related genes is dispersed in thousands of scientific papers, and there is still no study emphasizing on the systematic collection of ultraviolet-related genes. Methods: We collected ultraviolet-related genes and built this gene-centric database UVGD based on literature mining and manual curation. Literature mining was based on the ultraviolet-related abstracts downloaded from PubMed, and we obtained sentences in which ultraviolet keywords and genes co-occur at single-sentence level by using bio-entity recognizer. After that, manual curation was implemented in order to identify whether the genes are related to ultraviolet or not. Results: We built the ultraviolet-related knowledge base UVGD 1.0 (URL: http://biokb.ncpsb.org/UVGD/ ), which contains 663 ultraviolet-related genes, together with 17 associated biological processes, 117 associated phenotypes, and 2628 MeSH terms. Conclusion: UVGD is helpful to understand the ultraviolet-related biological processes in organisms and we believe it would be useful for biologists to study the responding mechanisms to ultraviolet.

FibroAtlas: A Database for the Exploration of Fibrotic Diseases and Their Genes.

BACKGROUND: Fibrosis is a highly dynamic process caused by prolonged injury, deregulation of the normal processes of wound healing, and extensive deposition of extracellular matrix (ECM) proteins. During fibrosis process, multiple genes interact with environmental factors. Over recent decades, tons of fibrosis-related genes have been identified to shed light on the particular clinical manifestations of this complex process. However, the genetics information about fibrosis is dispersed in lots of extensive literature. METHODS: We extracted data from literature abstracts in PubMed by text mining, and manually curated the literature and identified the evidence sentences. RESULTS: We presented FibroAtlas, which included 1,439 well-annotated fibrosis-associated genes. FibroAtlas 1.0 is the first attempt to build a nonredundant and comprehensive catalog of fibrosis-related genes with supporting evidence derived from curated published literature and allows us to have an overview of human fibrosis-related genes.

Revealing the metabolic characteristics of human embryonic stem cells by genome-scale metabolic modeling.

Embryonic stem cells (ESCs) are characterized by a dual capacity, self-renewal and pluripotency, which can be regulated by metabolism. A better understanding of ESC metabolism and regulatory mechanisms is pivotal for research into development, ageing, and cancer treatment. However, a systematic and comprehensive delineation of human ESC metabolism is still lacking. Here, we reconstructed the first genome-scale metabolic model (GEM) of human ESCs (hESCs). By GEM simulation and analyses, hESC global metabolic characteristics including essential metabolites and network motifs were identified. Potential metabolic subsystems responsible for self-renewal and pluripotency were also identified by analyses and experiments. This first GEM of hESCs provides a novel view and resource for stem cell metabolism research and will contribute to the elucidation of their metabolic characteristics.

AllerGAtlas 1.0: a human allergy-related genes database.

Database URL: http://biokb.ncpsb.org/AlleRGatlas/.

An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network.

The ubiquitination mediated by ubiquitin activating enzyme (E1), ubiquitin conjugating enzyme (E2), and ubiquitin ligase (E3) cascade is crucial to protein degradation, transcription regulation, and cell signaling in eukaryotic cells. The high specificity of ubiquitination is regulated by the interaction between E3 ubiquitin ligases and their target substrates. Unfortunately, the landscape of human E3-substrate network has not been systematically uncovered. Therefore, there is an urgent need to develop a high-throughput and efficient strategy to identify the E3-substrate interaction. To address this challenge, we develop a computational model based on multiple types of heterogeneous biological evidence to investigate the human E3-substrate interactions. Furthermore, we provide UbiBrowser as an integrated bioinformatics platform to predict and present the proteome-wide human E3-substrate interaction network ( http://ubibrowser.ncpsb.org ).Protein stability modulation by E3 ubiquitin ligases is an important layer of functional regulation, but screening for E3 ligase-substrate interactions is time-consuming and costly. Here, the authors take an in silico naïve Bayesian classifier approach to integrate multiple lines of evidence for E3-substrate prediction, enabling prediction of the proteome-wide human E3 ligase interaction network.

Dammarane Sapogenins Ameliorates Neurocognitive Functional Impairment Induced by Simulated Long-Duration Spaceflight.

Increasing evidence indicates the occurrence of cognitive impairment in astronauts under spaceflight compound conditions, but the underlying mechanisms and countermeasures need to be explored. In this study, we found that learning and memory abilities were significantly reduced in rats under a simulated long-duration spaceflight environment (SLSE), which includes microgravity, isolation confinement, noises, and altered circadian rhythms. Dammarane sapogenins (DS), alkaline hydrolyzed products of ginsenosides, can enhance cognition function by regulating brain neurotransmitter levels and inhibiting SLSE-induced neuronal injury. Bioinformatics combined with experimental verification identified that the PI3K-Akt-mTOR pathway was inhibited and the MAPK pathway was activated during SLSE-induced cognition dysfunction, whereas DS substantially ameliorated the changes in brain. These findings defined the characteristics of SLSE-induced cognitive decline and the mechanisms by which DS improves it. The results provide an effective candidate for improving cognitive function in spaceflight missions.

HisgAtlas 1.0: a human immunosuppression gene database.

Immunosuppression is body's state in which the activation or efficacy of immune system is weakened. It is associated with a wide spectrum of human diseases. In the last two decades, tremendous efforts have been made to elucidate the mechanism of hundreds of immunosuppression genes. Immunosuppression genes could be valuable drug targets or biomarkers for the immunotherapeutic treatment of different diseases. However, the information of all previously identified immunosuppression genes is dispersed in thousands of publications. Here, we provide the HisgAtlas database that collects 995 previously identified human immunosuppression genes using text mining and manual curation. We believe HisgAtlas will be a valuable resource to search human immunosuppression genes as well as to investigate their functions in further research. Database URL: http://biokb.ncpsb.org/HisgAtlas/.

BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine.

Traditional Chinese Medicine (TCM), with a history of thousands of years of clinical practice, is gaining more and more attention and application worldwide. And TCM-based new drug development, especially for the treatment of complex diseases is promising. However, owing to the TCM's diverse ingredients and their complex interaction with human body, it is still quite difficult to uncover its molecular mechanism, which greatly hinders the TCM modernization and internationalization. Here we developed the first online Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM). Its main functions include 1) TCM ingredients' target prediction; 2) functional analyses of targets including biological pathway, Gene Ontology functional term and disease enrichment analyses; 3) the visualization of ingredient-target-pathway/disease association network and KEGG biological pathway with highlighted targets; 4) comparison analysis of multiple TCMs. Finally, we applied BATMAN-TCM to Qishen Yiqi dripping Pill (QSYQ) and combined with subsequent experimental validation to reveal the functions of renin-angiotensin system responsible for QSYQ's cardioprotective effects for the first time. BATMAN-TCM will contribute to the understanding of the "multi-component, multi-target and multi-pathway" combinational therapeutic mechanism of TCM, and provide valuable clues for subsequent experimental validation, accelerating the elucidation of TCM's molecular mechanism. BATMAN-TCM is available at http://bionet.ncpsb.org/batman-tcm.

CAPER 3.0: A Scalable Cloud-Based System for Data-Intensive Analysis of Chromosome-Centric Human Proteome Project Data Sets.

The Chromosome-centric Human Proteome Project (C-HPP) aims to catalog genome-encoded proteins using a chromosome-by-chromosome strategy. As the C-HPP proceeds, the increasing requirement for data-intensive analysis of the MS/MS data poses a challenge to the proteomic community, especially small laboratories lacking computational infrastructure. To address this challenge, we have updated the previous CAPER browser into a higher version, CAPER 3.0, which is a scalable cloud-based system for data-intensive analysis of C-HPP data sets. CAPER 3.0 uses cloud computing technology to facilitate MS/MS-based peptide identification. In particular, it can use both public and private cloud, facilitating the analysis of C-HPP data sets. CAPER 3.0 provides a graphical user interface (GUI) to help users transfer data, configure jobs, track progress, and visualize the results comprehensively. These features enable users without programming expertise to easily conduct data-intensive analysis using CAPER 3.0. Here, we illustrate the usage of CAPER 3.0 with four specific mass spectral data-intensive problems: detecting novel peptides, identifying single amino acid variants (SAVs) derived from known missense mutations, identifying sample-specific SAVs, and identifying exon-skipping events. CAPER 3.0 is available at http://prodigy.bprc.ac.cn/caper3.