Kaempferol has potential anti-coronavirus disease 2019 (COVID-19) targets based on bioinformatics analyses and pharmacological effects on endotoxin-induced cytokine storm

Inhibitory effect of food-functioned phytochemicals on dysregulated inflammatory pathways triggered by SARS-CoV-2: a mechanistic review

Inflammatory cascades of the dysregulated inflammatory pathways in COVID-19 can cause excessive production of pro-inflammatory cytokines and chemokines leading to cytokine storm syndrome (CSS). The molecular cascades involved in the pathways may be targeted for discovery of new anti-inflammatory agents. Many plant extracts have been used clinically in the management of COVID-19, however, their immunosuppressive activities were mainly investigated based on in silico activity. Dietary flavonoids of the extracts such as quercetin, luteolin, kaempferol, naringenin, isorhamnetin, baicalein, wogonin, and rutin were commonly identified as responsible for their inhibitory effects. The present review critically analyzes the anti-inflammatory effects and mechanisms of phytochemicals, including dietary compounds against cytokine storm (CS) and hyperinflammation via inhibition of the altered inflammatory pathways triggered by SARS-CoV-2, published since the emergence of COVID-19 in December 2019. Only a few phytochemicals, mainly dietary compounds such as nanocurcumin, melatonin, quercetin, 6-shagoal, kaempferol, resveratrol, andrographolide, and colchicine have been investigated either in in silico or preliminary clinical studies to evaluate their anti-inflammatory effects against COVID-19. Sufficient pre-clinical studies on safety and efficacy of anti-inflammatory effects of the phytochemicals must be performed prior to proper clinical studies to develop them into therapeutic adjuvants in the prevention and treatmemt of COVID-19 symptoms.

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

Flavonoids derived from medicinal plants as a COVID-19 treatment

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 disease. Through its viral spike (S) protein, the virus enters and infects epithelial cells by utilizing angiotensin-converting enzyme 2 as a host cell's receptor protein. The COVID-19 pandemic had a profound impact on global public health and economies. Although various effective vaccinations and medications are now available to prevent and treat COVID-19, natural compounds derived from medicinal plants, particularly flavonoids, demonstrated therapeutic potential to treat COVID-19 disease. Flavonoids exhibit dual antiviral mechanisms: direct interference with viral invasion and inhibition of replication. Specifically, they target key viral molecules, particularly viral proteases, involved in infection. These compounds showcase significant immunomodulatory and anti-inflammatory properties, effectively inhibiting various inflammatory cytokines. Additionally, emerging evidence supports the potential of flavonoids to mitigate the progression of COVID-19 in individuals with obesity by positively influencing lipid metabolism. This review aims to elucidate the molecular structure of SARS-CoV-2 and the underlying mechanism of action of flavonoids on the virus. This study evaluates the potential anti-SARS-CoV-2 properties exhibited by flavonoid compounds, with a specific interest in their structure and mechanisms of action, as therapeutic applications for the prevention and treatment of COVID-19. Nevertheless, a significant portion of existing knowledge is based on theoretical frameworks and findings derived from in vitro investigations. Further research is required to better assess the effectiveness of flavonoids in combating SARS-CoV-2, with a particular emphasis on in vivo and clinical investigations.

Kaempferol attenuates wear particle-induced inflammatory osteolysis via JNK and p38-MAPK signaling pathways

ETHNOPHARMACOLOGICAL RELEVANCE

Wear particle-induced inflammatory osteoclast activation is a master contributor to periprosthetic osteolysis, which can cause pathological bone loss and destruction. Hence, inhibiting inflammation and osteoclastogenesis is an important strategy for preventing wear particle-induced osteolysis. To date, there are no FDA-approved non-surgical pharmacotherapies for arresting periprosthetic osteolysis. Kaempferol (KAE), a natural flavonol abundant in many traditional Chinese herbal medicines, has been shown to have protective effects against inflammatory bone diseases such as rheumatoid arthritis, but no previous study has evaluated the effects of KAE on wear particle-induced osteolysis.

AIM OF THE STUDY

The study aimed to investigate the effects of KAE on wear particle-induced inflammatory osteolysis and osteoclast activation, and further explore the underlying mechanisms.

MATERIALS AND METHODS

TiAl6V4 metal particles (TiPs) were retrieved from the prosthesis of patients who underwent revision hip arthroplasty due to aseptic loosening. A mouse calvarial osteolysis model was used to investigate the effects of KAE on wear particle-induced inflammatory osteolysis in vivo. Primary bone marrow-derived macrophages (BMMs) were used to explore the effects of KAE on osteoclast differentiation and bone-resorbing activity as well as the underlying mechanisms in vitro.

RESULTS

In the present study, we found that KAE alleviated wear particle-induced inflammatory bone loss in vivo and inhibited osteoclast differentiation and function in vitro. Furthermore, we revealed that KAE exerted anti-osteoclastogenic effects by downregulating JNK and p38-MAPK signaling as well as the downstream NFATc1 expression.

CONCLUSIONS

KAE is an alternative therapeutic agent for preventing and treating periprosthetic osteolysis and aseptic loosening.

Natural products for treating cytokine storm-related diseases: Therapeutic effects and mechanisms

BACKGROUND

A cytokine storm (CS) is a rapidly occurring, complex, and highly lethal systemic acute inflammatory response induced by pathogens and other factors. Currently, no clinical therapeutic drugs are available with a significant effect and minimal side effects. Given the pathogenesis of CS, natural products have become important resources for bioactive agents in the discovery of anti-CS drugs.

PURPOSE

This study aimed to provide guidance for preventing and treating CS-related diseases by reviewing the natural products identified to inhibit CS in recent years.

METHODS

A comprehensive literature review was conducted on CS and natural products, utilizing databases such as PubMed and Web of Science. The quality of the studies was evaluated and summarized for further analysis.

RESULTS

This study summarized more than 30 types of natural products, including 9 classes of flavonoids, phenols, and terpenoids, among others. In vivo and in vitro experiments demonstrated that these natural products could effectively inhibit CS via nuclear factor kappa-B, mitogen-activated protein kinase, and Mammalian target of rapamycin (mTOR) signaling pathways. Moreover, the enzyme inhibition assays revealed that more than 20 chemical components had the potential to inhibit ACE2, 3CL-protease, and papain-like protease activity. The experimental results were obtained using advanced technologies such as biochips and omics.

CONCLUSIONS

Various natural compounds in traditional Chinese medicine (TCM) extracts could directly or indirectly inhibit CS occurrence, potentially serving as effective drugs for treating CS-related diseases. This study may guide further exploration of the therapeutic effects and biochemical mechanisms of natural products on CS.

Deciphering the toxicity-effect relationship and action patterns of traditional Chinese medicines from a smart data perspective: a comprehensive review

In Chinese medicine, the primary considerations revolve around toxicity and effect. The clinical goal is to achieve maximize effect while minimizing toxicity. Nevertheless, both clinical and experimental research has revealed a distinct relationship between these two patterns of action in toxic Traditional Chinese Medicines (TCM). These TCM often exhibit characteristic "double-sided" or "multi-faceted" features under varying pathological conditions, transitioning between effective and toxic roles. This complexity adds a layer of challenge to unraveling the ultimate objectives of Traditional Chinese medicine. To address this complexity, various hypotheses have been proposed to explain the toxicity and effect of Traditional Chinese Medicines. These hypotheses encompass the magic shrapnel theory for effect, the adverse outcome pathway framework, and the indirect toxic theory for toxicity. This review primarily focuses on high-, medium-, and low-toxicity Traditional Chinese Medicines as listed in Chinese Pharmacopoeia. It aims to elucidate the essential intrinsic mechanisms and elements contributing to their toxicity and effectiveness. The critical factors influencing the mechanisms of toxicity and effect are the optimal dosage and duration of TCM administration. However, unraveling the toxic-effect relationships in TCM presents a formidable challenge due to its multi-target and multi-pathway mechanisms of action. We propose the integration of multi-omics technology to comprehensively analyze the fundamental metabolites, mechanisms of action, and toxic effects of TCM. This comprehensive approach can provide valuable insights into the intricate relationship between the effect and toxicity of these TCM.

Network pharmacology and molecular docking to scientifically validate the potential mechanism of Lonicerae japonicae flos in the clinical treatment of COVID-19

Using network pharmacology and molecular docking, we predicted the potential mechanisms of Lonicerae japonicae flos (LJF) therapy for COVID-19. A total of 493 component-related targets and 6,233 COVID-19-related genes were identified, and 267 core genes with overlapping of the two types of genes were identified. The target AKT1, CASP3, IL1B, IL6, PTGS2, TNF and JUN were the hub genes in PPI network according to MCODE score. Component-Target analysis showed the close relationship between targets and components. The results of functional enrichment analyses revealed that LJF exerted pharmacological effects on COVID-19 by regulating IL-17 signalling pathway, TNF signalling pathway, AGE-RAGE signalling pathway in diabetic complications, and Toll-like receptor signalling pathway. Finally, molecular docking confirmed a strong binding affinity between the 7 main active components with the hub genes. The findings suggested that beta-sitosterol, kaempferol and luteolin might be the promising leading components due to their good molecular docking scores.

Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing

Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.