NPBS database: a chemical data resource with relational data between natural products and biological sources

A critical assessment of bioactive compounds databases

Compound databases (DBs) are essential tools for drug discovery. The number of DBs in public domain is increasing, so it is important to analyze these DBs. In this article, the main characteristics of 64 DBs will be presented. The methodological strategy used was a literature search. To analyze the characteristics obtained in the review, the DBs were categorized into two subsections: Open Access and Commercial DBs. Open access includes generalist DBs (containing compounds of diverse origins), DBs with specific applicability, DBs exclusive to natural products and those containing compounds with specific pharmacological action. The literature review showed that there are challenges to making these repositories available, such as standardizing information curation practices and funding to maintain and sustain them.

Exploring the Antiviral Potential of Natural Compounds against Influenza: A Combined Computational and Experimental Approach

The influenza A virus nonstructural protein 1 (NS1), which is crucial for viral replication and immune evasion, has been identified as a significant drug target with substantial potential to contribute to the fight against influenza. The emergence of drug-resistant influenza A virus strains highlights the urgent need for novel therapeutics. This study proposes a combined theoretical criterion for the virtual screening of molecular libraries to identify candidate NS1 inhibitors. By applying the criterion to the ZINC Natural Product database, followed by ligand-based virtual screening and molecular docking, we proposed the most promising candidate as a potential NS1 inhibitor. Subsequently, the selected natural compound was experimentally evaluated, revealing measurable virus replication inhibition activity in cell culture. This approach offers a promising avenue for developing novel anti-influenza agents targeting the NS1 protein.

NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.

Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms

OBJECTIVES

This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA).

METHODS

GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics.

RESULTS

We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs.

CONCLUSION

Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.

Evidence of pyroptosis and ferroptosis extensively involved in autoimmune diseases at the single-cell transcriptome level

BACKGROUND

Approximately 8-9% of the world's population is affected by autoimmune diseases, and yet the mechanism of autoimmunity trigger is largely understudied. Two unique cell death modalities, ferroptosis and pyroptosis, provide a new perspective on the mechanisms leading to autoimmune diseases, and development of new treatment strategies.

METHODS

Using scRNA-seq datasets, the aberrant trend of ferroptosis and pyroptosis-related genes were analyzed in several representative autoimmune diseases (psoriasis, atopic dermatitis, vitiligo, multiple sclerosis, systemic sclerosis-associated interstitial lung disease, Crohn's disease, and experimental autoimmune orchitis). Cell line models were also assessed using bulk RNA-seq and qPCR.

RESULTS

A substantial difference was observed between normal and autoimmune disease samples involving ferroptosis and pyroptosis. In the present study, ferroptosis and pyroptosis showed an imbalance in different keratinocyte lineages of psoriatic skinin addition to a unique pyroptosis-sensitive keratinocyte subset in atopic dermatitis (AD) skin. The results also revealed that pyroptosis and ferroptosis are involved in epidermal melanocyte destruction in vitiligo. Aberrant ferroptosis has been detected in multiple sclerosis, systemic sclerosis-associated interstitial lung disease, Crohn's disease, and autoimmune orchitis. Cell line models adopted in the study also identified pro-inflammatory factors that can drive changes in ferroptosis and pyroptosis.

CONCLUSION

These results provide a unique perspective on the involvement of ferroptosis and pyroptosis in the pathological process of autoimmune diseases at the scRNA-seq level. IFN-γ is a critical inducer of pyroptosis sensitivity, and has been identified in two cell line models.

Predicting prognosis and clinical features of the tumor microenvironment based on ferroptosis score in patients with breast cancer

Ferroptosis genes have recently been reported to be involved in regulating the development of cancer, but their potential role in breast cancer (BRCA) is not fully understood. The purpose of this study is to systematically study the mechanism of ferroptosis in BRCA and its relationship with this cancer’s prognosis, cell infiltration, gene mutation, and other clinical features. In this study, The Cancer Genome Atlas breast cancer (TCGA-BRCA) database (UCSC Xena) was used to mine the ferroptosis genes related to BRCA patients, and the genes with prognostic value were screened by Cox regression analysis, which were then used to construct a prognostic model for scoring prognostic molecular risk. The relationships between ferroptosis score and prognosis, molecular typing, and clinical characteristics of BRCA were also analyzed. A total of 176 ferroptosis genes related to BRCA were retrieved from the database, 22 of which were found to be significantly related to BRCA prognosis after screening by single-factor Cox regression analysis (p < 0.01). Unsupervised clustering of samples was performed using factoextra, and two subgroups (ferroptosis cluster A and ferroptosis cluster B) with significant differences in prognosis were identified. Subsequently, single-factor Cox regression analysis and random forest dimensionality reduction were used to screen characteristic genes to construct a ferroptosis score model, which included a high ferroptosis score group and a low ferroptosis score group. The results showed that there were significant differences in ferroptosis scores between the ferroptosis cluster A and B groups. The prognosis of patients with low ferroptosis scores was poor, and the overall survival (OS) rate of patients with high ferroptosis scores was significantly higher, indicating that the prognosis of the sample can be well characterized based on calculated ferroptosis scores. Ferroptosis scores differed significantly according to patient age, TP53 and PIK3CA gene mutations, different PAM50 molecular types, and clinical stages. Ferroptosis activation plays a non-negligible role in tumor occurrence and development. Evaluating the ferroptosis score within BRCA will help advance our understanding of the infiltrating properties of cells in the tumor microenvironment and may guide more effective immunotherapy strategies.

Ferroptosis-related lncRNA signature predicts the prognosis and immune microenvironment of hepatocellular carcinoma

This study aimed to construct a ferroptosis-related lncRNA signature to probe the prognosis and immune infiltration of HCC patients. The Cancer Genome Atlas (TCGA) database was randomly divided into two parts, with two-thirds training and one-third testing sets. Univariate, multivariate, and least absolute selection operator (LASSO) Cox regression analyses were performed to establish a ferroptosis-related lncRNA signature. The prognostic signature was constructed by 6 ferroptosis-related lncRNAs (PCAT6, MKLN1-AS, POLH-AS1, LINC00942, AL031985.3, LINC00942) shows a promising clinical prediction value in patients with HCC. Patients with high-risk score indicated a poorer prognosis than patients with low-risk score were shown in the training set (p < 0.001) and testing set (p = 0.024). Principal component analysis (PCA) and nomogram were performed to verify the value of the prognostic signature. The area under curves (AUCs) for 1-, 3-, and 5-year survival rates were 0.784, 0.726, 0.699, respectively. Moreover, TCGA revealed that immune cell subpopulations and related functions, including cytolytic activity, MHC class I, type I and type II IFN response, were significantly different between the two risk groups. Immune checkpoints such as PDCD1, CTLA4, CD44, VTCN1 were also abnormally expressed between the two risk groups. This prognostic signature based on the ferroptosis-related lncRNAs may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with HCC.

In Silico Screening of Natural Compounds for Candidates 5HT6 Receptor Antagonists against Alzheimer's Disease

Alzheimer's disease (AD), a devastating neurodegenerative disease, is the focus of pharmacological research. One of the targets that attract the most attention for the potential therapy of AD is the serotonin 5HT6 receptor, which is the receptor situated exclusively in CNS on glutamatergic and GABAergic neurons. The neurochemical impact of this receptor supports the hypothesis about its role in cognitive, learning, and memory systems, which are of critical importance for AD. Natural products are a promising source of novel bioactive compounds with potential therapeutic potential as a 5HT6 receptor antagonist in the treatment of AD dementia. The ZINC-natural product database was in silico screened in order to find the candidate antagonists of 5-HT6 receptor against AD. A virtual screening protocol that includes both short-and long-range interactions between interacting molecules was employed. First, the EIIP/AQVN filter was applied for in silico screening of the ZINC database followed by 3D QSAR and molecular docking. Ten best candidate compounds were selected from the ZINC Natural Product database as potential 5HT6 Receptor antagonists and were proposed for further evaluation. The best candidate was evaluated by molecular dynamics simulations and free energy calculations.

Computational Analysis of Naturally Occurring Aristolochic Acid Analogues and Their Biological Sources

Aristolochic acids are known for nephrotoxicity, and implicated in multiple cancer types such as hepatocellular carcinomas demonstrated by recent studies. Natural products that are analogues to aristolochic acids have been constantly isolated from organisms; a larger chemical space of these compounds and a wider coverage of biological sources should be determined in consideration of the potential hazard of aristolochic acid analogues and the wide distribution of their biological sources in the nature. Therefore, we carried out an in silico research of naturally occurring aristolochic acid analogues and their biological sources, as a supplement to existing studies. The result shows a chemical space of 238 naturally occurring aristolochic acid analogues that are present in 175 species of biological sources including 44 traditional medicines. With the computational estimation for toxicity and the implication in hazard assessment of a biological source with the presence of aristolochic acid analogues, we propose that additional awareness should be raised to the public for avoidance of toxic species, especially those that are used as herbal medicines and easily accessible.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.