Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols

C ucumis melo compounds: A new avenue for ALR-2 inhibition in diabetes mellitus

Diabetes mellitus (DM) is a prominent contributor to morbidity and mortality in developed nations, primarily attributable to vascular complications such as atherothrombosis occurring in the coronary arteries. Aldose reductase (ALR2), the main enzyme in the polyol pathway, catalyzes the conversion of glucose to sorbitol, leading to a significant buildup of reactive oxygen species in different tissues. It is therefore a prime candidate for therapeutic targeting, and extensive study is currently underway to discover novel natural compounds that can inhibit it. Cucumis melo (C. melo) has a long history as a lipid-lowering ethanopharmaceutical plant. In this study, compounds derived from C. melo were computationally evaluated as possible lead candidates. Various computational filtering methods were employed to assess the drug-like properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles of the compounds. The compounds were subsequently addressed to analysis of their interactions, molecular docking, and molecular dynamics simulation studies. When compared to the conventional therapeutic compounds, three compounds exhibited enhanced binding affinity and intra-molecular residue interactions, resulting in increased stability and specificity. Consequently, four potent inhibitors, namely PubChem CIDs 119205, 65373, 6184, and 332427, have been identified. These inhibitors exhibit promising potential as pharmacological targets for the advancement of novel ALR-2 inhibitors.

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Revealing Comprehensive Food Functionalities and Mechanisms of Action through Machine Learning

Foods possess a range of unexplored functionalities; however, fully identifying these functions through empirical means presents significant challenges. In this study, we have proposed an in silico approach to comprehensively predict the functionalities of foods, encompassing even processed foods. This prediction is accomplished through the utilization of machine learning on biomedical big data. Our focus revolves around disease-related protein pathways, wherein we statistically evaluate how the constituent compounds collaboratively regulate these pathways. The proposed method has been employed across 876 foods and 83 diseases, leading to an extensive revelation of both food functionalities and their underlying operational mechanisms. Additionally, this approach identifies food combinations that potentially affect molecular pathways based on interrelationships between food functions within disease-related pathways. Our proposed method holds potential for advancing preventive healthcare.

Unraveling the Evolutionary Diet Mismatch and Its Contribution to the Deterioration of Body Composition

Over the millennia, patterns of food consumption have changed; however, foods were always whole foods. Ultra-processed foods (UPFs) have been a very recent development and have become the primary food source for many people. The purpose of this review is to propose the hypothesis that, forsaking the evolutionary dietary environment, and its complex milieu of compounds resulting in an extensive metabolome, contributes to chronic disease in modern humans. This evolutionary metabolome may have contributed to the success of early hominins. This hypothesis is based on the following assumptions: (1) whole foods promote health, (2) essential nutrients cannot explain all the benefits of whole foods, (3) UPFs are much lower in phytonutrients and other compounds compared to whole foods, and (4) evolutionary diets contributed to a more diverse metabolome. Evidence will be presented to support this hypothesis. Nutrition is a matter of systems biology, and investigating the evolutionary metabolome, as compared to the metabolome of modern humans, will help elucidate the hidden connections between diet and health. The effect of the diet on the metabolome may also help shape future dietary guidelines, and help define healthy foods.

Network Medicine: A Potential Approach for Virtual Drug Screening

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Unambiguous Characterization of Commercial Natural (Dihydro)phenanthrene Compounds Is Vital in the Discovery of AMPK Activators

These days, easy access to commercially available (poly)phenolic compounds has expanded the scope of potential research beyond the field of chemistry, particularly in the area of their bioactivity. However, the quality of these compounds is often overlooked or not even considered. This issue is illustrated in this study through the example of (dihydro)phenanthrenes, a group of natural products present in yams, as AMP-activated protein kinase (AMPK) activators. A study conducted in our group on a series of compounds, fully characterized using a combination of chemical synthesis, NMR and MS techniques, provided evidence that the conclusions of a previous study were erroneous, likely due to the use of a misidentified commercial compound by its supplier. Furthermore, we demonstrated that additional representatives of the (dihydro)phenanthrene phytochemical classes were able to directly activate AMPK, avoiding the risk of misinterpretation of results based on analysis of a single compound alone.

Systematic exploration of therapeutic effects and key mechanisms of Panax ginseng using network-based approaches

Background

Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms.

Purpose

We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood.

Methods

Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model.

Results

We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms.

Conclusion

our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

Photochemical Strategies toward Precision Targeting against Multidrug-Resistant Bacterial Infections

Infectious diseases pose a serious threat and a substantial economic burden on global human and public health security, especially with the frequent emergence of multidrug-resistant (MDR) bacteria in clinical settings. In response to this urgent need, various photobased anti-infectious therapies have been reported lately. This Review explores and discusses several photochemical targeted antibacterial therapeutic strategies for addressing bacterial infections regardless of their antibiotic susceptibility. In contrast to conventional photobased therapies, these approaches facilitate precise targeting of pathogenic bacteria and/or infectious microenvironments, effectively minimizing toxicity to mammalian cells and surrounding healthy tissues. The highlighted therapies include photodynamic therapy, photocatalytic therapy, photothermal therapy, endogenous pigments-based photobleaching therapy, and polyphenols-based photo-oxidation therapy. This comprehensive exploration aims to offer updated information to facilitate the development of effective, convenient, safe, and alternative strategies to counter the growing threat of MDR bacteria in the future.

Self-assembled nanodrug delivery systems for anti-cancer drugs from traditional Chinese medicine

Traditional Chinese medicine (TCM) is a combination of raw herbs and herbal extracts with a plethora of documented beneficial bioactivities, which has unique advantages in anti-tumor therapy, and many of its major bioactive molecules have been identified in recent years due to advances in chemical separation and structural analysis. However, the major chemical classes of plant-derived bioactive compounds frequently possess chemical properties, including poor water solubility, stability, and bioavailability, that limit their therapeutic application. Alternatively, natural small molecules (NSMs) containing these components possess modifiable groups, multiple action sites, hydrophobic side chains, and a rigid skeleton with self-assembly properties that can be exploited to construct self-assembled nanoparticles with therapeutic effects superior to their individual constituents. For instance, the construction of a self-assembled nanodrug delivery system can effectively overcome the strong hydrophobicity and poor in vivo stability of NSMs, thereby greatly improving their bioavailability and enhancing their anti-tumor efficacy. This review summarizes the self-assembly methods, mechanisms, and applications of a variety of NSMs, including terpenoids, flavonoids, alkaloids, polyphenols, and saponins, providing a theoretical basis for the subsequent research on NSMs and the development of SANDDS.

scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BACKGROUND

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

METHODS

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

RESULTS

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

CONCLUSIONS

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package ( https://github.com/SDTC-CPMed/scDrugPrio ).

Golden bile powder prevents drunkenness and alcohol-induced liver injury in mice via the gut microbiota and metabolic modulation

BACKGROUND

Drunkenness and alcoholic liver disease (ALD) are critical public health issues associated with significant morbidity and mortality due to chronic overconsumption of alcohol. Traditional remedies, such as bear bile powder, have been historically acclaimed for their hepatoprotective properties. This study assessed the efficacy of a biotransformed bear bile powder known as golden bile powder (GBP) in alleviating alcohol-induced drunkenness and ALD.

METHODS

A murine model was engineered to simulate alcohol drunkenness and acute hepatic injury through the administration of a 50% ethanol solution. Intervention with GBP and its effects on alcohol-related symptoms were scrutinized, by employing an integrative approach that encompasses serum metabolomics, network medicine, and gut microbiota profiling to elucidate the protective mechanisms of GBP.

RESULTS

GBP administration significantly delayed the onset of drunkenness and decreased the duration of ethanol-induced inebriation in mice. Enhanced liver cell recovery was indicated by increased hepatic aldehyde dehydrogenase levels and superoxide dismutase activity, along with significant decreases in the serum alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, triglyceride, and total cholesterol levels (P < 0.05). These biochemical alterations suggest diminished hepatic damage and enhanced lipid homeostasis. Microbiota analysis via 16S rDNA sequencing revealed significant changes in gut microbial diversity and composition following alcohol exposure, and these changes were effectively reversed by GBP treatment. Metabolomic analyses demonstrated that GBP normalized the alcohol-induced perturbations in phospholipids, fatty acids, and bile acids. Correlation assessments linked distinct microbial genera to serum bile acid profiles, indicating that the protective efficacy of GBP may be attributable to modulatory effects on metabolism and the gut microbiota composition. Network medicine insights suggest the prominence of two active agents in GBP as critical for addressing drunkenness and ALD.

CONCLUSION

GBP is a potent intervention for alcohol-induced pathology and offers hepatoprotective benefits, at least in part, through the modulation of the gut microbiota and related metabolic cascades.

FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks

Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.

Characterization of PKCα-rutin interactions and their application as a treatment strategy for pulmonary arterial hypertension by inhibiting ferroptosis

As a common plant-derived dietary flavonoid, rutin receives widespread attention because of its good antioxidant bioactivities. Protein kinase Cα (PKCα) is a serine/threonine kinase that is involved in uncountable cellular processes, among which ferroptosis, a novel form of cell death, is triggered by lipid peroxidation and has been reported to be associated with pulmonary arterial hypertension (PAH). But it is still not well appreciated how rutin inhibits ferroptosis in PAH and what function PKCα has in this process. In this study, we first observed whether rutin could prevent PAH by attenuating ferroptosis with a PAH animal model and pulmonary artery smooth muscle cells (PASMCs) under hypoxia. Mitochondrial metabolomics and network pharmacology were employed to clarify the metabolic alterations and screen target proteins, and the results showed that PKCα was a vital node in rutin regulating mitochondrial metabolism related to ferroptosis in PAH. Based on molecular docking and multispectral analysis, we found that rutin could directly interact with PKCα through hydrogen bonds, which could induce static quenching, and then influence the secondary structure of PKCα. In conclusion, these findings mainly point to a novel mechanism that rutin protects PAH rats by modifying the structure and altering the activity of PKCα, and thus suppressing ferroptosis. This work reveals that the interaction behaviors between small molecules and bio-macromolecules are a critical factor to develop natural biological active ingredients and gives an insight into the potential applications of flavonoids in health and disease.

Promises of anionic calix[n]arenes in life science: State of the art in 2023

Because they hold together molecules by means of non-covalent interactions - relatively weak and thus, potentially reversible - the anionic calixarenes have become an interesting tool for efficiently binding a large range of ligands - from gases to large organic molecules. Being highly water soluble and conveniently biocompatible, they showed growing interest for many interdisciplinary fields, particularly in biology and medicine. Thanks to their intrinsic conical shape, they provide suitable platforms, from vesicles to bilayers. This is a valuable characteristic, as so they mimic the biologically functional architectures. The anionic calixarenes propose efficient alternatives for overcoming the limitations linked to drug delivery and bioavailability, as well as drug resistance along with limiting the undesirable side effects. Moreover, the dynamic non-covalent binding with the drugs enables predictable and on demand drug release, controlled by the stimuli present in the targeted environment. This particular feature instigated the use of these versatile, stimuli-responsive compounds for sensing biomarkers of diverse pathologies. The present review describes the recent achievements of the anionic calixarenes in the field of life science, from drug carriers to biomedical engineering, with a particular outlook on their applications for the diagnosis and treatment of different pathologies.

Natural Polyphenolic Nanodots for Alzheimer's Disease Treatment

The abnormal amyloid-β accumulation is essential and obbligato in Alzheimer's disease pathogenesis and natural polyphenols exhibit great potential as amyloid aggregation inhibitors. However, the poor metabolic stability, low bioavailability, and weak blood-brain barrier crossing ability of natural polyphenol molecules fail to meet clinical needs. Here, a universal protocol to prepare natural polyphenolic nanodots is developed by heating in aqueous solution without unacceptable additives. The nanodots are able to not only inhibit amyloid-β fibrillization and trigger the fibril disaggregation, but mitigate the amyloid-β-plaque-induced cascade impairments including normalizing oxidative microenvironment, altering microglial polarization, and rescuing neuronal death and synaptic loss, which results in significant improvements in recognition and cognition deficits in transgenic mice. More importantly, natural polyphenolic nanodots possess stronger antiamyloidogenic performance compared with small molecule, as well as penetrate the blood-brain barrier. The excellent biocompatibility further guarantees the potential of natural polyphenolic nanodots for clinical applications. It is expected that natural polyphenolic nanodots provide an attractive paradigm to support the development of the therapeutics for Alzheimer's disease.

Systemic Convergent Multitarget Interactions of Plant Polyphenols Revealed by Affinity-Based Protein Profiling of Bone Cells Using C-Glucosidic Vescal(ag)in-Bearing Chemoproteomic Probes

The ellagitannins vescalagin and vescalin, known as actin-dependent inhibitors of osteoclastic bone resorption, were mounted onto chemical probes to explore their interactions with bone cell proteins by means of affinity-based chemoproteomics and bioinformatics. The chemical reactivity of the pyrogallol units of these polyphenols toward oxidation into electrophilic ortho-quinones was exploited using NaIO4 to promote the covalent capture of target proteins, notably those expressed at lower abundance and those interacting with polyphenols at low-to-moderate levels of affinity. Different assays revealed the multitarget nature of both ellagitannins, with 100-370 statistically significant proteins captured by their corresponding probes. A much higher number of proteins were captured from osteoclasts than from osteoblasts. Bioinformatic analyses unveiled a preference for the capture of proteins having phosphorylated ligands and GTPase regulators and enabled the identification of 33 potential target proteins with systemic relevance to osteoclast differentiation and activity, as well as to the regulation of actin dynamics.

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

Background

Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs.

Methods

Here, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.

Results

scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn's disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn's disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment.

Conclusion

We propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrio's potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

Food Allergens: When Friends Become Foes-Caveats and Opportunities for Oral Immunotherapy Based on Deactivation Methods

Food allergies represent a serious health concern and, since the 1990s, they have risen gradually in high-income countries. Unfortunately, the problem is complex because genetic, epigenetic, and environmental factors may be collectively involved. Prevention and diagnoses have not yet evolved into efficacious therapies. Identification and control of allergens present in edible substances hold promise for multi-purpose biomedical approaches, including oral immunotherapy. This review highlights recent studies and methods to modify the otherwise innocuous native proteins in most subjects, and how oral treatments targeting immune responses could help cancel out the potential risks in hypersensitive individuals, especially children. We have focused on some physical methods that can easily be conducted, along with chemo-enzymatic modifications of allergens by means of peptides and phytochemicals in particular. The latter, accessible from naturally-occurring substances, provide an added value to hypoallergenic matrices employing vegetal wastes, a point where food chemistry meets sustainable goals as well.

Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture

BACKGROUND

Ziziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement.

METHODS

Mice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients.

RESULTS

ZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF-MS. Also, we have constructed an online computation platform.

CONCLUSION

Our data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.

Antioxidant and Wound Healing Bioactive Potential of Extracts Obtained from Bark and Needles of Softwood Species

Interest in the extraction of phytochemical bioactive compounds, especially polyphenols from biomass, has recently increased due to their valuable biological potential as natural sources of antioxidants, which could be used in a wide range of applications, from foods and pharmaceuticals to green polymers and bio-based materials. The present research study aimed to provide a comprehensive chemical characterization of the phytochemical composition of forest biomass (bark and needles) of softwood species (Picea abies L., H. Karst., and Abies alba Mill.) and to investigate their in vitro antioxidant and antimicrobial activities to assess their potential in treating and healing infected chronic wounds. The DPPH radical-scavenging method and P-LD were used for a mechanistic explanation of the biomolecular effects of the investigated bioactive compounds. (+)-Catechin, epicatechin, rutin, myricetin, 4 hydroxybenzoic and p-cumaric acids, kaempherol, and apigenin were the main quantified polyphenols in coniferous biomass (in quantities around 100 µg/g). Also, numerous phenolic acids, flavonoids, stilbenes, terpenes, lignans, secoiridoids, and indanes with antioxidant, antimicrobial, anti-inflammatory, antihemolytic, and anti-carcinogenic potential were identified. The Abies alba needle extract was more toxic to microbial strains than the eukaryotic cells that provide its active wound healing principles. In this context, developing industrial upscaling strategies is imperative for the long-term success of biorefineries and incorporating them as part of a circular bio-economy.

Network medicine framework identified drug-repurposing opportunities of pharmaco-active compounds of Angelica acutiloba (Siebold & Zucc.) Kitag. for skin aging

Increasing incidence of skin aging has highlighted the importance of identifying effective drugs with repurposed opportunities for skin aging. We aimed to identify pharmaco-active compounds with drug-repurposing opportunities for skin aging from Angelica acutiloba (Siebold & Zucc.) Kitag. (AAK). The proximity of network medicine framework (NMF) firstly identified 8 key AAK compounds with repurposed opportunities for skin aging, which may exert by regulating 29 differentially expressed genes (DGEs) of skin aging, including 13 up-regulated targets and 16 down-regulated targets. Connectivity MAP (cMAP) analysis revealed 8 key compounds were involved in regulating the process of cell proliferation and apoptosis, mitochondrial energy metabolism and oxidative stress of skin aging. Molecular docking analysis showed that 8 key compounds had a high docked ability with AR, BCHE, HPGD and PI3, which were identified as specific biomarker for the diagnosis of skin aging. Finally, the mechanisms of these key compounds were predicted to be involved in inhibiting autophagy pathway and activating Phospholipase D signaling pathway. In conclusion, this study firstly elucidated the drug-repurposing opportunities of AAK compounds for skin aging, providing a theoretical reference for identifying repurposing drugs from Chinese medicine and new insights for our future research.

Natural Products for the Prevention, Treatment and Progression of Breast Cancer

In this review, we summarize the most used natural products as useful adjuvants in BC by clarifying how these products may play a critical role in the prevention, treatment and progression of this disease. BC is the leading cancer, in terms of incidence, that affects women. The epidemiology and pathophysiology of BC were widely reported. Inflammation and cancer are known to influence each other in several tumors. In the case of BC, the inflammatory component precedes the development of the neoplasm through a slowly increasing and prolonged inflammation that also favors its growth. BC therapy involves a multidisciplinary approach comprising surgery, radiotherapy and chemotherapy. There are numerous observations that showed that the effects of some natural substances, which, in integration with the classic protocols, can be used not only for prevention or integration in order to prevent recurrences and induce a state of chemoquiescence but also as chemo- and radiosensitizers during classic therapy.

Estimated dietary intake of polyphenols from cereal foods and associated lifestyle and demographic factors in the Melbourne Collaborative Cohort Study

Cereal foods are consumed globally and are important sources of polyphenols with potential health benefits, yet dietary intakes are unclear. We aimed to calculate the dietary intakes of polyphenols from cereal foods in the Melbourne Collaborative Cohort Study (MCCS), and describe intakes by demographic and lifestyle factors. We estimated intakes of alkylresorcinols, lignans and phenolic acids in n = 39,892 eligible MCCS participants, using baseline dietary data (1990-1994) from a 121-item FFQ containing 17 cereal foods, matched to a polyphenol database developed from published literature and Phenol-Explorer Database. Intakes were estimated within groups according to lifestyle and demographic factors. The median (25th-75th percentile) intake of total polyphenols from cereal foods was 86.9 mg/day (51.4-155.8). The most consumed compounds were phenolic acids, with a median intake of 67.1 mg (39.5-118.8), followed by alkylresorcinols of 19.7 mg (10.8-34.6). Lignans made the smallest contribution of 0.50 mg (0.13-0.87). Higher polyphenol intakes were associated with higher relative socio-economic advantage and prudent lifestyles, including lower body mass index (BMI), non-smoking and higher physical activity scores. The findings based on polyphenol data specifically matched to the FFQ provide new information on intakes of cereal polyphenols, and how they might vary according to lifestyle and demographic factors.

Heart Failure Evolution Model Based on Anomalous Diffusion Theory

The unexpectable variations of the diagnosed disease symptoms are quite often observed during medical diagnosis. In stochastics, such behavior is called "grey swan" or "black swan" as synonyms of sudden, unpredictable change. Evolution of the disease's symptoms is usually described by means of Markov processes, where dependency on process history is neglected. The common expectation is that such processes are Gaussian. It is demonstrated here that medical observation can be described as a Markov process and is non-Gaussian. Presented non-Gaussian processes have "fat tail" probability density distribution (pdf). "Fat tail" permits a slight change of probability density distribution and triggers an unexpectable big variation of the diagnosed parameter. Such "fat tail" solution is delivered by the anomalous diffusion model applied here to describe disease evolution and to explain the possible presence of "swans" mentioned above. The proposed model has been obtained as solution of the Fractal Fokker-Planck equation (FFPE). The paper shows a comparison of the results of the theoretical model of anomalous diffusion with experimental results of clinical studies using bioimpedance measurements in cardiology. This allows us to consider the practical usefulness of the proposed solutions.

Quercetin Is An Active Agent in Berries against Neurodegenerative Diseases Progression through Modulation of Nrf2/HO1

Berries are well-known fruits for their antioxidant effects due to their high content of flavonoids, and quercetin is one of the potent bioactive flavonoids. Although oxidative stress is an inevitable outcome in cells due to energy uptake and metabolism and other factors, excessive oxidative stress is considered a pivotal mediator for the cell death and leads to the progression of neurodegenerative diseases (NDDs). Furthermore, oxidative stress triggers inflammation that leads to neuronal cell loss. Alzheimer's, Parkinson's, Huntington's disease, amyotrophic lateral sclerosis, multiple sclerosis, and so on are the main neurodegenerative diseases. Hence, AD and PD are the most affected NDDs and cause the most lethality without any effective cure. Since AD and PD are the most common NDDs, therefore, in this study, we will describe the effect of oxidative stress on AD and PD. Targeting oxidative stress could be a very effective way to prevent and cure NDDs. Thus, the nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO1) are potent endogenous antioxidant modulatory pathways, which also show cytoprotective activities. Modulation of Nrf2/HO1 signaling pathways through a biological approach could be an effective way to treat with NDDs. Quercetin is a natural polyphenol, which protects neurodegeneration, remarkably by suppressing oxidative stress and inflammation. Thus, quercetin could be a very effective agent against NDDs. We will discuss the benefits and challenges of quercetin to treat against NDDs, focusing on molecular biology.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research

Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.

DIAMIN: a software library for the distributed analysis of large-scale molecular interaction networks

Background

Huge amounts of molecular interaction data are continuously produced and stored in public databases. Although many bioinformatics tools have been proposed in the literature for their analysis, based on their modeling through different types of biological networks, several problems still remain unsolved when the problem turns on a large scale.

Results

We propose , that is, a high-level software library to facilitate the development of applications for the efficient analysis of large-scale molecular interaction networks. relies on distributed computing, and it is implemented in Java upon the framework Apache Spark. It delivers a set of functionalities implementing different tasks on an abstract representation of very large graphs, providing a built-in support for methods and algorithms commonly used to analyze these networks. has been tested on data retrieved from two of the most used molecular interactions databases, resulting to be highly efficient and scalable. As shown by different provided examples, can be exploited by users without any distributed programming experience, in order to perform various types of data analysis, and to implement new algorithms based on its primitives.

Conclusions

The proposed has been proved to be successful in allowing users to solve specific biological problems that can be modeled relying on biological networks, by using its functionalities. The software is freely available and this will hopefully allow its rapid diffusion through the scientific community, to solve both specific data analysis and more complex tasks.

Elucidation of the potential antioxidant compound and mechanism of mung bean using network pharmacology and in vitro anti-oxidative activity

Mung bean is rich in bioactive components, but the main compound and pharmacological mechanism in reducing oxidative and free radical damage are unclear. Network pharmacology and 2,2′-azino-bis-3-ethylbenzthiazoline-6-sulfonic acid (ABTS) radical scavenging activities were employed to uncover the antioxidant mechanism of potentially active compounds, considering the interactions between mung bean targets and oxidative and free radical damage. These key targets were analyzed by protein–protein interactions (PPIs), and key genes were used to find the biological pathway and therapeutic mechanism by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The results showed that five antioxidant components and 18 mung bean targets were screened. β-carotene and vitexin both played a crucial role in mung bean against oxidative and free radical damage, and the ABTS radical scavenging activities of β-carotene and vitexin were 94.84 and 87.79%, which were equivalent to those of vitamin C. Key targets may be AR, HSP90AA1, MYC, and CASP3 for mung bean to exert antioxidant activity. GO and KEGG indicated that mung bean may mainly act on thyroid hormone signaling pathway, estrogen signaling pathway, p53 signaling pathway, etc. In vitro antioxidant activity tests showed that the bioactive ingredients of mung beans had great antioxidant activity. Network pharmacology analysis also revealed the underlying molecular mechanisms of oxidative and free radical damage. This study provides new insights and evidence to explore the bioactive compounds and biological functions of food cereals and legumes, as well as a reference for the functional evaluation of food ingredients and the development of functional foods.

Visualizing novel connections and genetic similarities across diseases using a network-medicine based approach

Understanding the genetic relationships between human disorders could lead to better treatment and prevention strategies, especially for individuals with multiple comorbidities. A common resource for studying genetic-disease relationships is the GWAS Catalog, a large and well curated repository of SNP-trait associations from various studies and populations. Some of these populations are contained within mega-biobanks such as the Million Veteran Program (MVP), which has enabled the genetic classification of several diseases in a large well-characterized and heterogeneous population. Here we aim to provide a network of the genetic relationships among diseases and to demonstrate the utility of quantifying the extent to which a given resource such as MVP has contributed to the discovery of such relations. We use a network-based approach to evaluate shared variants among thousands of traits in the GWAS Catalog repository. Our results indicate many more novel disease relationships that did not exist in early studies and demonstrate that the network can reveal clusters of diseases mechanistically related. Finally, we show novel disease connections that emerge when MVP data is included, highlighting methodology that can be used to indicate the contributions of a given biobank.

Natural Polyphenols-Platinum Nanocomplexes Stimulate Immune System for Combination Cancer Therapy

Nanocarriers have been employed extensively to enhance drug delivery efficacy and reduce the side effect. However, carrier materials for drug delivery have challenging aspects, including safety concerns, low drug content, complexity in preparation, and low reproducibility. Herein, we propose a facile, universal, and green preparation way to use natural polyphenols to build platinum nanocomplex with stable structure, proper size, and high Pt content. The nanocomplexes are constructed by metal-polyphenol coordination using natural polyphenols and 1,2-diaminocyclohexane-Pt (II), enabling dual-responsive drug release behavior. For proof of concept, we demonstrate the antitumor activity of the Pt nanocomplex using a representative tannic acid-Pt nanocomplex (denoted as PTI). PTI can induce intensive tumor cell apoptosis, trigger immunogenic cell death (ICD), remarkably promote cytotoxic T lymphocytes (CTLs) infiltration in tumors, and significantly reduce immunosuppression of the tumor microenvironments, thus stimulating potent antitumor immune responses and showing effective antitumor activity by synergizing immune checkpoint blockade (ICB) therapy.

Regulation of appetite-related neuropeptides by Panax ginseng: A novel approach for obesity treatment

Obesity is a primary factor provoking various chronic disorders, including cardiovascular disease, diabetes, and cancer, and causes the death of 2.8 million individuals each year. Diet, physical activity, medications, and surgery are the main therapies for overweightness and obesity. During weight loss therapy, a decrease in energy stores activates appetite signaling pathways under the regulation of neuropeptides, including anorexigenic [corticotropin-releasing hormone, proopiomelanocortin (POMC), cholecystokinin (CCK), and cocaine- and amphetamine-regulated transcript] and orexigenic [agouti-related protein (AgRP), neuropeptide Y (NPY), and melanin-concentrating hormone] neuropeptides, which increase food intake and lead to failure in attaining weight loss goals. Ginseng and ginsenosides reverse these signaling pathways by suppressing orexigenic neuropeptides (NPY and AgRP) and provoking anorexigenic neuropeptides (CCK and POMC), which prevent the increase in food intake. Moreover, the results of network pharmacology analysis have revealed that constituents of ginseng radix, including campesterol, beta-elemene, ginsenoside Rb1, biotin, and pantothenic acid, are highly correlated with neuropeptide genes that regulate energy balance and food intake, including ADIPOQ, NAMPT, UBL5, NUCB2, LEP, CCK, GAST, IGF1, RLN1, PENK, PDYN, and POMC. Based on previous studies and network pharmacology analysis data, ginseng and its compounds may be a potent source for obesity treatment by regulating neuropeptides associated with appetite.

Identifying Candidate Flavonoids for Non-Alcoholic Fatty Liver Disease by Network-Based Strategy

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD.

Yes SIR! On the structure-inactivity relationships in drug discovery

In analogy with structure-activity relationships (SARs), which are at the core of medicinal chemistry, studying structure-inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish 'activity' and 'inactivity' with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled 'active' and 'inactive' compounds.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Targeting Lipid Rafts as a Rapid Screening Strategy for Potential Antiadipogenic Polyphenols along with the Structure-Activity Relationship and Mechanism Elucidation

Obesity is a global public health problem that endangers human health, and a rapid search for compounds with antiadipogenic activity could provide solutions to overcome this problem. Polyphenols are potential antiadipogenic compounds, but the screening strategy, structure-activity relationship (SAR), and elucidation of their mechanisms of action remain poorly understood because of the high diversity of polyphenols. Lipid rafts, enriched with sphingolipids and cholesterol, are considered a potential target of polyphenols for the regulation of cellular processes and diseases. Here, a novel rapid screening active polyphenol strategy that targets the lipid rafts using molecular dynamic simulation was developed and validated by 3T3-L1 preadipocyte assay. The screening strategy is high-throughput, inexpensive, reagent-free, and effort saving. In addition, the SAR and mechanisms of action mediating the differentiation-inhibition of the preadipocyte by polyphenols were well elucidated by utilizing multiple technologies, such as "raft-like liposomes" systems, giant plasma membrane vesicles, noninvasive lipid raft probes, and ultrahigh-resolution microscopy. High inhibitory-activity polyphenols could penetrate deeper into the hydrophobic lipid center, in an inverted V-shaped manner or by insertion of galloyl groups into rafts, thus disrupting the ordered domain of lipid rafts. In contrast, the medium and low inhibitory-activity polyphenols could only localize on the surface of lipid rafts, exerting slight and the weakest interference with a lipid raft structure, respectively. The combined use of reliable technologies could yield new knowledge on the SAR and the molecular mechanisms of polyphenols.

Recurrent gestational diabetes : Breaking the transgenerational cycle with lifestyle modification

PURPOSE

This literature review is aimed at examining the benefits of lifestyle modifications in preventing recurrent gestational diabetes (GDM). Worldwide GDM affects approximately 16.2% of all pregnancies with significant maternal, fetal and neonatal complications. Almost two thirds of pregnant women with GDM will develop type 2 diabetes mellitus (T2DM) in the years following pregnancy. The proportion of women affected by GDM is on the rise and reflects increasing trends in T2DM as well as adult and childhood obesity.

METHODS

Using predefined subject headings, we searched for relevant articles from the PubMed, Scopus, and Cochrane databases.

RESULTS

For high-risk women lifestyle modifications, such as dietary and exercise changes, are the mainstay of treatment to reduce negative outcomes for both women and their pregnancies. This includes reducing the incidence of recurrent GDM and future T2DM by intervening during pregnancy and in the postnatal period.

CONCLUSION

This review provides an overview of the literature to date, discusses different targeted approaches and how these interventions can optimise their benefits, and where further research is required.

Porous Cellulose Acetate/Block Copolymer Membranes for the Recovery of Polyphenolic Compounds from Aquatic Environments

Polyphenols are natural compounds with strong antioxidant properties synthesized by plants and widely distributed in plant tissues. They compose a broad class of compounds that are commonly employed for multiple applications such as food, pharmaceutical, adhesives, biomedical, agricultural, and industrial purposes. Runoffs from these sources result in the introduction of polyphenols into aquatic environments where they further transform into highly toxic pollutants that can negatively affect aquatic ecosystems and humans. Therefore, the development of extraction and remediation methods for such compounds must be addressed. This study describes the identification and operation of a method to recover polyphenolic compounds from water environments by utilizing membrane-based separation. Composite membranes derived from electrospun cellulose acetate (CA) fibers and diblock copolymer (DiBCP) PEO-b-P4VP were prepared to evaluate the adsorption of polyphenolic compounds from aqueous environments. The highly porous CA fibers were developed using the electrospinning technique, and the fabricated DiBCP/CA membranes were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared (FT-IR) spectroscopy, and tensile testing. Finally, the ability of the composite membranes to adsorb the soluble polyphenolic compounds catechol (CAT) and gallic acid (GA), from a wetland environment, was studied via batch adsorption experiments and by solid-phase extraction (SPE). Results revealed a successful recovery of both polyphenols, at concentrations within the parts per million (ppm) range, from the aqueous media. This suggests a novel approach to recover these compounds to prevent their transformation into toxic pollutants upon entrance to water environments.

Epigallocatechin 3-gallate: From green tea to cancer therapeutics

Epigallocatechin 3-gallate (EGCG) possesses various biological functions, including anti-cancer and anti-inflammatory properties. EGCG is an abundant polyphenolic component originating from green tea extract that has exhibited versatile bioactivities in combating several cancers. This review highlights the pharmacological features of EGCG and its therapeutic implications in cancer and other metabolic diseases. It modulates numerous signaling pathways, regulating cells' undesired survival and proliferation, thus imparting strong tumor chemopreventive and therapeutic effects. EGCG initiates cell death through the intrinsic pathway and causes inhibition of EGFR, STAT3, and ERK pathways in several cancers. EGCG alters and inhibits ERK1/2, NF-κB, and Akt-mediated signaling, altering the Bcl-2 family proteins ratio and activating caspases in tumor cells. This review focuses on anti-cancer, anti-oxidant, anti-inflammatory, anti-angiogenesis, and apoptotic effects of EGCG. We further highlighted the potential of EGCG in different types of cancer, emphasizing clinical trials formulations that further improve our understanding of the therapeutic management of cancer and inflammatory diseases.

Diversity: current and prospective secondary metabolites for nutrition and medicine

Plants have been used as sources of food, feed and medicine for millennia. The ever-increasing population has, however, dramatically increased the burden on our arable land to meet nutritional demand. Concomitantly, and in part due to poor nutrition, we are faced with massive increases in chronic diseases, meaning the need for medicine has also increased. Here, we look back on research in these areas, surveying the polyphenols as a case study for health-conferring metabolites. We conclude that the tools that will allow us to breed more nutritious crops are all at hand. We stress that collaboration between plant and medical research needs to be intensified in order to improve our understanding of the bioactivities. In doing so, we attempt to draw a roadmap for the use of plants for mid-21st Century human health.

Do Certain Flavonoid IMPS Have a Vital Function?

Flavonoids are a vast group of metabolites that are essential for vascular plant physiology and, thus, occur ubiquitously in plant-based/-derived foods. The solitary designation of thousands of known flavonoids hides the fact that their metabolomes are structurally highly diverse, consist of disparate subgroups, yet undergo a certain degree of metabolic interconversion. Unsurprisingly, flavonoids have been an important theme in nutrition research. Already in the 1930s, it was discovered that the ability of synthetic Vitamin C to treat scurvy was inferior to that of plant extracts containing Vitamin C. Subsequent experimental evidence led to the proposal of Vitamin P (permeability) as an essential phytochemical nutrient. However, attempts to isolate and characterize Vitamin P gave confusing and sometimes irreproducible results, which today can be interpreted as rooted in the unrecognized (residual) complexity of the intervention materials. Over the years, primarily flavonoids (and some coumarins) were known as having Vitamin P-like activity. More recently, in a NAPRALERT meta-analysis, essentially all of these Vitamin P candidates were identified as IMPs (Invalid/Improbable/Interfering Metabolic Panaceas). While the historic inability to define a single compound and specific mode of action led to general skepticism about the Vitamin P proposition for "bioflavonoids," the more logical conclusion is that several abundant and metabolically labile plant constituents fill this essential role in human nutrition at the interface of vitamins, cofactors, and micronutrients. Reviewing 100+ years of the multilingual Vitamin P and C literature provides the rationales for this conclusion and new perspectives for future research.

Polyphenols, the Healthy Brand of Olive Oil: Insights and Perspectives

Given their beneficial potential on human health, plant food bioactive molecules are important components influencing nutrition. Polyphenols have been widely acknowledged for their potentially protective role against several complex diseases. In particular, the polyphenols of olive oil (OOPs) emerge as the key components of many healthy diets and have been widely studied for their beneficial properties. The qualitative and quantitative profile defining the composition of olive oil phenolic molecules as well as their absorbance and metabolism once ingested are key aspects that need to be considered to fully understand the health potential of these molecules. In this review, we provide an overview of the key aspects influencing these variations by focusing on the factors influencing the biosynthesis of OOPs and the findings about their absorption and metabolism. Despite the encouraging evidence, the health potential of OOPs is still debated due to limitations in current studies. Clinical trials are necessary to fully understand and validate the beneficial effects of olive oil and OOPs on human health. We provide an update of the clinical trials based on olive oil and/or OOPs that aim to understand their beneficial effects. Tailored studies are needed to standardize the polyphenolic distribution and understand the variables associated with phenol-enriched OO. An in-depth knowledge of the steps that occur following polyphenol ingestion may reveal useful insights to be used in clinical settings for the prevention and treatment of many diseases.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Tessaria absinthioides (Hook. & Arn.) DC. (Asteraceae) Decoction Improves the Hypercholesterolemia and Alters the Expression of LXRs in Rat Liver and Hypothalamus

Chronic high-fat diet consumption induces hypercholesterolemia. The effect of Tessaria absinthioides (Hook. & Arn.) DC. (Asteraceae) was studied on the levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and triglycerides, and on the expression of liver X receptors (LXRs) in a hypercholesterolemic model. Adult male rats received a normal diet (ND) or a high-fat diet (HFD; normal diet + bovine fat + cholesterol). After 14 days, rats received water (W) or a decoction of the aerial parts of T. absinthioides (Ta; 10% w/v) for 2, 4, or 6 weeks. Four and six weeks of Ta improved the levels of TC and HDL-c in HFD. After 6 weeks of Ta, the expression of LXRs in HFD was the same as that in ND in both tissues. The Ta chemical profile was studied with an ultrahigh resolution liquid chromatography Orbitrap MS analysis (UHPLC-PDA-OT-MS/MS). Fifty-one compounds were identified, of which twelve are reported for the first time. Among these compounds, caffeoylquinic acid and its derivatives could modify the lipid profile and the expression of LXRs. This is the first in vivo report of T. absinthioides, which may be a potential candidate against hypercholesterolemia.

In Vitro and In Silico Characterization of G-Protein Coupled Receptor (GPCR) Targets of Phlorofucofuroeckol-A and Dieckol

Phlorotannins are polyphenolic compounds in marine alga, especially the brown algae. Among numerous phlorotannins, dieckol and phlorofucofuroeckol-A (PFF-A) are the major ones and despite a wider biological activity profile, knowledge of the G protein-coupled receptor (GPCR) targets of these phlorotannins is lacking. This study explores prime GPCR targets of the two phlorotannins. In silico proteocheminformatics modeling predicted twenty major protein targets and in vitro functional assays showed a good agonist effect at the α2C adrenergic receptor (α2CAR) and an antagonist effect at the adenosine 2A receptor (A2AR), δ-opioid receptor (δ-OPR), glucagon-like peptide-1 receptor (GLP-1R), and 5-hydroxytryptamine 1A receptor (5-TH1AR) of both phlorotannins. Besides, dieckol showed an antagonist effect at the vasopressin 1A receptor (V1AR) and PFF-A showed a promising agonist effect at the cannabinoid 1 receptor and an antagonist effect at V1AR. In silico molecular docking simulation enabled us to investigate and identify distinct binding features of these phlorotannins to the target proteins. The docking results suggested that dieckol and PFF-A bind to the crystal structures of the proteins with good affinity involving key interacting amino acid residues comparable to reference ligands. Overall, the present study suggests α2CAR, A2AR, δ-OPR, GLP-1R, 5-TH1AR, CB1R, and V1AR as prime receptor targets of dieckol and PFF-A.

Network medicine framework for identifying drug-repurposing opportunities for COVID-19

The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.