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Network pharmacology and experimental evidence: ERK/CREB/BDNF signaling pathway is involved in the antidepressive roles of Kaiyu Zhishen decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Major Depressive Disorder (MDD) emerges as a complex psychosomatic condition, notable for its considerable suicidality and mortality rates. Increasing evidence suggests the efficacy of Chinese herbal medicine in mitigating depression symptoms and offsetting the adverse effects associated with conventional Western therapeutics. Notably, clinical trials have revealed the adjunctive antidepressant potential of Kaiyu Zhishen Decoction (KZD) alongside Western medication. However, the standalone antidepressant efficacy of KZD and its underlying mechanisms merit in-depth investigation.

AIM OF THE STUDY

This research aims to elucidate the impact of KZD on MDD and delineate its mechanistic pathways through integrated network pharmacological assessments and empirical in vitro and in vivo analyses.

MATERIALS AND METHODS

To ascertain the optimal antidepressant dosage and mechanism of KZD, a Chronic Unpredictable Mild Stress (CUMS)-induced depression model in mice was established to evaluate depressive behaviors. High-Performance Liquid Chromatography (HPLC) and network pharmacological approaches were employed to predict KZD's antidepressant mechanisms. Subsequently, hippocampal samples were subjected to 4D-DIA proteomic sequencing and validated through Western blot, immunofluorescence, Nissl staining, and pathway antagonist applications. Additionally, cortisol-stimulated PC12 cells were utilized to simulate neuronal damage, analyzing protein and mRNA levels of MAPK-related signals and cell proliferation markers.

RESULTS

The integration of network pharmacology and HPLC identified kaempferol and quercetin as KZD's principal active compounds for MDD treatment. Proteomic and network pharmacological KEGG pathway analyses indicated the MAPK signaling pathway as a critical regulatory mechanism for KZD's therapeutic effect on MDD. KZD was observed to mitigate CUMS-induced upregulation of p-ERK/ERK, CREB, and BDNF protein expressions in hippocampal cells by attenuating oxidative stress, thereby ameliorating neuronal damage and exerting antidepressant effects. The administration of PD98059 counteracted KZD's improvements in depression-like behaviors and downregulated p-ERK/ERK and BDNF protein expressions in the hippocampus.

CONCLUSIONS

This investigation corroborates KZD's pivotal, dose-dependent role in antidepressant activity. Both in vivo and in vitro experiments demonstrate KZD's capacity to modulate the ERK-CREB-BDNF signaling pathway by diminishing ROS expression induced by oxidative stress, enhancing neuronal repair, and thus, manifesting antidepressant properties. Accordingly, KZD represents a promising herbal candidate for further antidepressant research.

Explore the active ingredients and potential mechanism of action on Actinidia arguta leaves against T2DM by integration of serum pharmacochemistry and network pharmacology

BACKGROUND

Actinidia arguta leaves (AAL) are traditionally consumed as a vegetable and as tea in folk China and Korea. Previous studies have reported the anti-diabetic effect of AAL, but its bioactive components and mechanism of action are still unclear.

AIM OF THE STUDY

This study aims to identify the hypoglycemic active components of AAL by combining serum pharmacochemistry and network pharmacology and to elucidate its possible mechanism of action.

METHODS

Firstly, the effective components in mice serum samples were characterized by UPLC-Q/TOF-MSE. Furthermore, based on these active ingredients, network pharmacology analysis was performed to establish an "H-C-T-P-D" interaction network and reveal possible biological mechanisms. Finally, the affinity between serum AAL components and the main proteins in the important pathways above was investigated through molecular docking analysis.

RESULTS

Serum pharmacochemistry analysis showed that 69 compounds in the serum samples were identified, including 23 prototypes and 46 metabolites. The metabolic reactions mainly included deglycosylation, dehydration, hydrogenation, methylation, acetylation, glucuronidation, and sulfation. Network pharmacology analysis showed that the key components quercetin, pinoresinol diglucoside, and 5-O-trans-p-coumaroyl quinic acid butyl ester mainly acted on the core targets PTGS2, HRAS, RELA, PRKCA, and BCL2 targets and through the PI3K-Akt signaling pathway, endocrine resistance, and MAPK signaling pathway to exert a hypoglycemic effect. Likewise, molecular docking results showed that the three potential active ingredients had good binding effects on the five key targets.

CONCLUSION

This study provides a basis for elucidating the pharmacodynamic substance basis of AA against T2DM and further exploring the mechanism of action.

Construction of IRAK4 inhibitor activity prediction model based on machine learning

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R2 of 0.829, an MAE of 0.317, and an RMSE of 0.460 in independent testing. To further validate the model's generalization ability, we tested it on 90 IRAK4 inhibitors collected in 2023. Subsequently, we applied the model to predict the activity of 13,268 compounds with docking scores less than - 9.503 kcal/mol. These compounds were initially screened from a pool of 1.6 million molecules in the chemdiv database through high-throughput molecular docking. Among these, 259 compounds with predicted pIC50 values greater than or equal to 8.00 were identified. We then performed ADMET predictions on these selected compounds. Finally, through a rigorous screening process, we identified 34 compounds that adhere to the four complementary drug-likeness rules, making them promising candidates for further investigation. Additionally, molecular dynamics simulations confirmed the stable binding of the screened compounds to the IRAK4 protein. Overall, this work presents a machine learning model for accurate prediction of IRAK4 inhibitor activity and offers new insights for subsequent structure-guided design of novel IRAK4 inhibitors.

RING 4.0: faster residue interaction networks with novel interaction types across over 35,000 different chemical structures

Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/.

PROSCA: an online platform for humanized scaffold mining facilitating rational protein engineering

Protein scaffolds with small size, high stability and low immunogenicity show important applications in the field of protein engineering and design. However, no relevant computational platform has been reported yet to mining such scaffolds with the desired properties from massive protein structures in human body. Here, we developed PROSCA, a structure-based online platform dedicated to explore the space of the entire human proteome, and to discovery new privileged protein scaffolds with potential engineering value that have never been noticed. PROSCA accepts structure of protein as an input, which can be subsequently aligned with a certain class of protein structures (e.g. the human proteome either from experientially resolved or AlphaFold2 predicted structures, and the human proteins belonging to specific families or domains), and outputs humanized protein scaffolds which are structurally similar with the input protein as well as other related important information such as families, sequences, structures and expression level in human tissues. Through PROSCA, the user can also get excellent experience in visualizations of protein structures and expression overviews, and download the figures and tables of results which can be customized according to the user's needs. Along with the advanced protein engineering and selection technologies, PROSCA will facilitate the rational design of new functional proteins with privileged scaffolds. PROSCA is freely available at https://idrblab.org/prosca/.

Metagenome-derived SusD-homologs affiliated with Bacteroidota bind to synthetic polymers

Starch utilization system (Sus)D-homologs are well known for their carbohydrate-binding capabilities and are part of the sus operon in microorganisms affiliated with the phylum Bacteroidota. Until now, SusD-like proteins have been characterized regarding their affinity toward natural polymers. In this study, three metagenomic SusD homologs (designated SusD1, SusD38489, and SusD70111) were identified and tested with respect to binding to natural and non-natural polymers. SusD1 and SusD38489 are cellulose-binding modules, while SusD70111 preferentially binds chitin. Employing translational fusion proteins with superfolder GFP (sfGFP), pull-down assays, and surface plasmon resonance (SPR) has provided evidence for binding to polyethylene terephthalate (PET) and other synthetic polymers. Structural analysis suggested that a Trp triad might be involved in protein adsorption. Mutation of these residues to Ala resulted in an impaired adsorption to microcrystalline cellulose (MC), but not so to PET and other synthetic polymers. We believe that the characterized SusDs, alongside the methods and considerations presented in this work, will aid further research regarding bioremediation of plastics.

IMPORTANCE

SusD1 and SusD38489 can be considered for further applications regarding their putative adsorption toward fossil-fuel based polymers. This is the first time that SusD homologs from the polysaccharide utilization loci (PUL), largely described for the phylum Bacteroidota, are characterized as synthetic polymer-binding proteins.

Impact of Protein Corona Formation and Polystyrene Nanoparticle Functionalisation on the Interaction with Dynamic Biomimetic Membranes Comprising of Integrin

Plastics, omnipresent in the environment, have become a global concern due to their durability and limited biodegradability, especially in the form of microparticles and nanoparticles. Polystyrene (PS), a key plastic type, is susceptible to fragmentation and surface alterations induced by environmental factors or industrial processes. With widespread human exposure through pollution and diverse industrial applications, understanding the physiological impact of PS, particularly in nanoparticle form (PS-NPs), is crucial. This study focuses on the interaction of PS-NPs with model blood proteins, emphasising the formation of a protein corona, and explores the subsequent contact with platelet membrane mimetics using experimental and theoretical approaches. The investigation involves αIIbβ3-expressing cells and biomimetic membranes, enabling real-time and label-free nanoscale precision. By employing quartz-crystal microbalance with dissipation monitoring studies, the concentration-dependent cytotoxic effects of differently functionalised ~210 nm PS-NPs on HEK293 cells overexpressing αIIbβ3 are evaluated in detail. The study unveils insights into the molecular details of PS-NP interaction with supported lipid bilayers, demonstrating that a protein corona formed in the presence of exemplary blood proteins offers protection against membrane damage, mitigating PS-NP cytotoxicity.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

Evolution of orphan and atypical histidine kinases and response regulators for microbial signaling diversity

Two-component signaling systems (TCS) are the predominant means of microbes for sensing and responding to environmental stimuli. Typically, TCS is comprised of a sensor histidine kinase (HK) and a cognate response regulator (RR), which might have coevolved together. They usually involve the phosphoryl transfer signaling mechanism. However, there are also some orphan and atypical HK and RR homologs, and their evolutionary origins are still not very clear. They are not associated with cognate pairs or lack the conserved residues for phosphoryl transfer, but they could receive or respond to signals from other regulators. The objective of this study is to reveal the evolutionary history of these orphan and atypical HK and RR homologs. Structural, domain, sequence, and phylogenetic analyses indicated that their evolution process might undergo gene duplication, divergence, and domain shuffling. Meanwhile, lateral gene transfer might also be involved for their gene distribution. Evolution of orphan and atypical HK and RR homologs have increased their signaling diversity, which could be helpful for microbial adaption in complex environments.

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

Spectroscopic investigation and structural simulation in human serum albumin with hydroxychloroquine/Silybum marianum and a possible potential COVID-19 drug candidate

In this study, the interaction between human serum albumin (HSA) and the hydroxychloroquine/Silybum marianum (HCQ/SM) mixture was investigated using various techniques. The observed high binding constant (Kb) and Stern-Volmer quenching constant (KSV) indicate a strong binding affinity between the HCQ/SM mixture and HSA. The circular dichroism (CD) analysis revealed that HCQ/SM induced conformational changes in the secondary structure of HSA, leading to a decrease in the α-helical content. UV-Vis analysis exhibited a slight redshift, indicating that the HCQ/SM mixture could adapt to the flexible structure of HSA. The experimental results demonstrated the significant conformational changes in HSA upon binding with HCQ/SM. Theoretical studies were carried out using molecular dynamics simulation via the Gromacs simulation package to explore insights into the drug interaction with HSA-binding sites. Furthermore, molecular docking studies demonstrated that HCQ/SM-HSA exhibited favorable docking scores with the receptor (5FUZ), suggesting a potential therapeutic relevance in combating COVID-19 with a value of -6.24 kcal mol-1. HCQ/SM exhibited stronger interaction with both SARS-CoV-2 virus main proteases compared to favipiravir. Ultimately, the experimental data and molecular docking analysis presented in this research offer valuable insights into the pharmaceutical and biological properties of HCQ/SM mixtures when interacting with serum albumin.

Bioinspired chitosan based functionalization of biomedical implant surfaces for enhanced hemocompatibility, antioxidation and anticoagulation potential: an in silico and in vitro study

Endowing implanted biomaterials with better hemocompatibility, anticoagulation, antioxidant and antiplatelet adhesion is necessary because of their potential to trigger activation of multiple reactive mechanisms including coagulation cascade and potentially causing serious adverse clinical events like late thrombosis. Active ingredients from natural sources including Foeniculum vulgare, Angelica sinensis, and Cinnamomum verum have the ability to inhibit the coagulation cascade and thrombus formation around biomedical implants. These properties are of interest for the development of a novel drug for biomedical implants to potentially solve the current blood clotting and coagulation problems which lead to stent thrombosis. The objective of this study was to incorporate different anticoagulants from natural sources into a degradable matrix of chitosan with varying concentrations ranging from 5% to 15% and a composite containing all three drugs. The presence of anticoagulant constituents was identified using GC-MS. Subsequently, all the compositions were characterized principally by using Fourier transform infrared spectroscopy and scanning electron microscopy while the drug release profile was determined using UV-spectrometry for a 30 days immersion period. The results indicated an initial burst release which was subsequently followed by the sustained release pattern. Compared to heparin loaded chitosan, DPPH and hemolysis tests revealed better blood compatibility of natural drug loaded films. Moreover, the anticoagulation activity of natural drugs was equivalent to the heparin loaded film; however, through docking, the mechanism of inhibition of the coagulation cascade of the novel drug was found to be through blocking the extrinsic pathway. The study suggested that the proposed drug composite expresses an optimum composition which may be a practicable and appropriate candidate for biomedical implant coatings.

Uncovering the ferroptosis related mechanism of laduviglusib in the cell-type-specific targets of the striatum in Huntington's disease

Huntington's disease (HD) is a dominantly inherited neurodegenerative disorder featured by abnormal movements, arising from the extensive neuronal loss and glial dysfunction in the striatum. Although the causes and pathogenetic mechanisms of HD are well established, the development of disease-modifying pharmacological therapies for HD remains a formidable challenge. Laduviglusib has demonstrated neuroprotective effects through the enhancement of mitochondrial function in the striatum of HD animal models. Ferroptosis is a nonapoptotic form of cell death that occurs as a consequence of lethal iron-dependent lipid peroxidation and mitochondrial dysfunction. However, the ferroptosis-related mechanisms underlying the neuroprotective effects of laduviglusib in the striatum of HD patients remain largely uncharted. In this study, we leveraged single-nucleus RNA sequencing data obtained from the striatum of HD patients in stages 2-4 to identify differentially expressed genes within distinct cell-type. We subsequently integrated these differentially expressed genes of HD, laduviglusib target genes and ferroptosis-related genes to predict the ferroptosis-related mechanisms underpinning the neuroprotective effects of laduviglusib in HD patients. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses unveiled that the effects of laduviglusib on direct pathway striatal projection neurons (dSPNs) is mainly associated with Th17 cell differentiation pathways. Conversely, its impact on indirect pathway striatal projection neurons (iSPNs) extends to the Neurotrophin signaling pathway, FoxO signaling pathway, and reactive oxygen species pathway. In microglia, laduviglusib appears to contribute to HD pathology via mechanisms related to Th17 cell differentiation and the FoxO signaling pathway. Further, molecular docking results indicated favorable binding of laduviglusib with PARP1 (associated with dSPNs and iSPNs), SCD (associated with astrocytes), ALOX5 (associated with microglia), and HIF1A (associated with dSPNs, iSPNs, and microglia). In addition, the KEGG results suggest that laduviglusib may enhance mitochondrial function and protect against neuronal loss by targeting ferroptosis-related signaling pathways, particularly mediated by ALOX5 in microglia. These findings provide valuable insights into the potential mechanisms through which laduviglusib exerts its effects on distinct cell-types within the HD striatum.

Multisubstrate specificity shaped the complex evolution of the aminotransferase family across the tree of life

Aminotransferases (ATs) are an ancient enzyme family that play central roles in core nitrogen metabolism, essential to all organisms. However, many of the AT enzyme functions remain poorly defined, limiting our fundamental understanding of the nitrogen metabolic networks that exist in different organisms. Here, we traced the deep evolutionary history of the AT family by analyzing AT enzymes from 90 species spanning the tree of life (ToL). We found that each organism has maintained a relatively small and constant number of ATs. Mapping the distribution of ATs across the ToL uncovered that many essential AT reactions are carried out by taxon-specific AT enzymes due to wide-spread nonorthologous gene displacements. This complex evolutionary history explains the difficulty of homology-based AT functional prediction. Biochemical characterization of diverse aromatic ATs further revealed their broad substrate specificity, unlike other core metabolic enzymes that evolved to catalyze specific reactions today. Interestingly, however, we found that these AT enzymes that diverged over billion years share common signatures of multisubstrate specificity by employing different nonconserved active site residues. These findings illustrate that AT family enzymes had leveraged their inherent substrate promiscuity to maintain a small yet distinct set of multifunctional AT enzymes in different taxa. This evolutionary history of versatile ATs likely contributed to the establishment of robust and diverse nitrogen metabolic networks that exist throughout the ToL. The study provides a critical foundation to systematically determine diverse AT functions and underlying nitrogen metabolic networks across the ToL.

The regulation of GSH/GPX4-mediated lipid accumulation confirms that schisandra polysaccharides should be valued equally as lignans

ETHNOPHARMACOLOGICAL RELEVANCE

Acetaminophen (APAP) induced liver injury (AILI) is a common cause of clinical hepatic damage and even acute liver failure. Our previous research has shown that Schisandra chinensis lignan extract (SLE) can exert a hepatoprotective effect by regulating lipid metabolism. Although polysaccharides from Schisandra chinensis (S. chinensis), like lignans, are important components of S. chinensis, their pharmacological activity and target effects on AILI have not yet been explored.

AIM OF THE STUDY

This study aims to quantitatively reveal the role of SCP in the pharmacological activity of S. chinensis, and further explore the pharmacological components, potential action targets and mechanisms of S. chinensis in treating AILI.

MATERIALS AND METHODS

The therapeutic effect of SCP on AILI was systematically determined via comparing the efficacy of SCP and SLE on in vitro and in vivo models. Network pharmacology, molecular docking and multi-omics techniques were then used to screen and verify the action targets of S. chinensis against AILI.

RESULTS

SCP intervention could significantly improve AILI, and the therapeutic effect was comparable to that of SLE. Notably, the combination of SCP and SLE did not produce mutual antagonistic effects. Subsequently, we found that both SCP and SLE could significantly reverse the down-regulation of GPX4 caused by the APAP modeling, and then further improving lipid metabolism abnormalities.

CONCLUSIONS

Hepatoprotective effects of SCP and SLE is most correlated with their regulation of GSH/GPX4-mediated lipid accumulation. This is the first exploration of the hepatoprotective effect and potential mechanism of SCP in treating AILI, which is crucial for fully utilizing S. chinensis and developing promising AILI therapeutic agents.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Discovery of azaleatin as a potential allosteric inhibitor for dengue NS2B-NS3 protease using in vitro and in silico studies

The rise in dengue cases in tropical and sub-tropical areas has become a significant health concern. At present, there is no definitive cure for dengue fever, which underscores the importance of identifying potent inhibitors. Dengue NS2B-NS3 protease is the prime drug target due to its vital function for replication. Quercetin, a flavone, has anti-dengue virus properties but is limited by low bioavailability. Previous studies have shown that methoxy substitution in flavones improves bioavailability and metabolic stability. Azaleatin is a derivative of quercetin with a methoxy substitution at the C5 position, however its ability to inhibit dengue is unknown. In this study, azaleatin was investigated for its inhibition against dengue NS2B-NS3 protease using in vitro and in silico techniques. The fluorescence assay was used to determine the IC50 value and inhibition kinetics. The molecular interaction between azaleatin and NS2B-NS3 was studied using CB-Dock2 and AutoDock Vina. The complex's stability was then analysed using GROMACS. Besides, the ADMETlab 2.0 was utilized to predict pharmacokinetic of the azaleatin. Results showed that azaleatin inhibits dengue NS2B-NS3 protease non-competitively with a Ki of 26.82 µg/ml and an IC50 of 38 µg/ml. Molecular docking indicated binding of the azaleatin to the allosteric pocket of NS2B-NS3 with a docking score of -8.2 kcal/mol. Azaleatin was found stable in the pocket along 100 ns, supporting its inhibitory mode. The compound has favourable pharmacokinetic profiles and conformed to Lipinski's Rule of Five. Taken together, azaleatin inhibits NS2B-NS3 protease in a non-competitive mode, suggesting its potential as safer anti-dengue compound.Communicated by Ramaswamy H. Sarma.

Mechanism of 7H-Dibenzo[c,g]carbazole metabolism in cytochrome P450 1A1: Insights from computational studies

7H-Dibenzo[c,g]carbazole (DBC) is a prevalent environmental contaminant that induces tumorigenesis in several experimental animals. Recently, it has been utilized to develop high-performance solar cells and organic phosphorescent materials. It is imperative to strengthen investigations of DBC metabolism to understand its potential risks to human health. In this study, human CYP1A1 was employed as the metabolic enzyme to investigate the metabolic mechanism of DBC by molecular docking, molecular dynamics (MD) simulation, and quantum mechanical (QM) calculation. The results indicate that DBC binds to CYP1A1 in two modes (mode 1 and mode 2) mainly through nonpolar solvation energies (ΔGnonpolar). The formation of the two binding modes is attributed to the anchoring effect of the hydrogen bond formed by DBC with Asp320 (mode 1) or Ser116 (mode 2). Mode 1 is a "reactive" conformation, while mode 2 is not considered a "reactive" conformation. C5 is identified as the dominant site, and the pyrrole nitrogen cannot participate in the metabolism. DBC is metabolized mainly by a distinct electrophilic addition-rearrangement mechanism, with an energy barrier of 21.74 kcal/mol. The results provide meaningful insights into the biometabolic process of DBC and contribute to understanding its environmental effects and health risks.

Suppressing Anaphase-Promoting Complex/Cyclosome-Cell Division Cycle 20 Activity to Enhance the Effectiveness of Anti-Cancer Drugs That Induce Multipolar Mitotic Spindles

Paclitaxel induces multipolar spindles at clinically relevant doses but does not substantially increase mitotic indices. Paclitaxel's anti-cancer effects are hypothesized to occur by promoting chromosome mis-segregation on multipolar spindles leading to apoptosis, necrosis and cyclic-GMP-AMP Synthase-Stimulator of Interferon Genes (cGAS-STING) pathway activation in daughter cells, leading to secretion of type I interferon (IFN) and immunogenic cell death. Eribulin and vinorelbine have also been reported to cause increases in multipolar spindles in cancer cells. Recently, suppression of Anaphase-Promoting Complex/Cyclosome-Cell Division Cycle 20 (APC/C-CDC20) activity using CRISPR/Cas9 mutagenesis has been reported to increase sensitivity to Kinesin Family 18a (KIF18a) inhibition, which functions to suppress multipolar mitotic spindles in cancer cells. We propose that a way to enhance the effectiveness of anti-cancer agents that increase multipolar spindles is by suppressing the APC/C-CDC20 to delay, but not block, anaphase entry. Delaying anaphase entry in genomically unstable cells may enhance multipolar spindle-induced cell death. In genomically stable healthy human cells, delayed anaphase entry may suppress the level of multipolar spindles induced by anti-cancer drugs and lower mitotic cytotoxicity. We outline specific combinations of molecules to investigate that may achieve the goal of enhancing the effectiveness of anti-cancer agents.

Identification of Prospective Ebola Virus VP35 and VP40 Protein Inhibitors from Myxobacterial Natural Products

The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fast growth phenotypes are achieved through optimal transcriptomic allocation, in which cells must balance tradeoffs in resource allocation between diverse functions. One such balance between stress readiness and unbridled growth in E. coli has been termed the fear versus greed (f/g) tradeoff. Two specific RNA polymerase (RNAP) mutations observed in adaptation to fast growth have been previously shown to affect the f/g tradeoff, suggesting that genetic adaptations may be primed to control f/g resource allocation. Here, we conduct a greatly expanded study of the genetic control of the f/g tradeoff across diverse conditions. We introduced 12 RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and obtained expression profiles of each. We found that these single RNAP mutation strains resulted in large shifts in the f/g tradeoff primarily in the RpoS regulon and ribosomal genes, likely through modifying RNAP-DNA interactions. Two of these mutations additionally caused condition-specific transcriptional adaptations. While this tradeoff was previously characterized by the RpoS regulon and ribosomal expression, we find that the GAD regulon plays an important role in stress readiness and ppGpp in translation activity, expanding the scope of the tradeoff. A phylogenetic analysis found the greed-related genes of the tradeoff present in numerous bacterial species. The results suggest that the f/g tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.IMPORTANCETo increase growth, E. coli must raise ribosomal content at the expense of non-growth functions. Previous studies have linked RNAP mutations to this transcriptional shift and increased growth but were focused on only two mutations found in the protein's central region. RNAP mutations, however, commonly occur over a large structural range. To explore RNAP mutations' impact, we have introduced 12 RNAP mutations found in laboratory evolution experiments and obtained expression profiles of each. The mutations nearly universally increased growth rates by adjusting said tradeoff away from non-growth functions. In addition to this shift, a few caused condition-specific adaptations. We explored the prevalence of this tradeoff across phylogeny and found it to be a widespread and conserved trend among bacteria.

Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study

BACKGROUND

N-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene.

RESULTS

Out of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer.

CONCLUSION

This in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

Qijiao Shengbai Capsule alleviated leukopenia by interfering leukotriene pathway: Integrated network study of multi-omics

BACKGROUND

Leukopenia could be induced by chemotherapy, which leads to bone marrow suppression and even affects the therapeutic progression of cancer. Qijiao Shengbai Capsule (QSC) has been used for the treatment of leukopenia in clinic, but its bioactive components and mechanisms have not yet been elucidated clearly.

PURPOSE

This study aimed to elucidate the molecular mechanisms of QSC in treating leukopenia.

STUDY DESIGN

Serum pharmacochemistry, multi-omics, network pharmacology, and validation experiment were combined to study the effect of QSC in murine leukopenia model.

METHODS

First, UPLC-QTOF-MS was used to clarify the absorbed components of QSC. Then, cyclophosphamide (CTX) was used to induce mice model with leukopenia, and the therapeutic efficacy of QSC was assessed by an integrative approach of multi-omics and network pharmacology strategy. Finally, molecular mechanisms and potential therapeutic targets were identified by validated experiments.

RESULTS

121 compounds absorbed in vivo were identified. QSC significantly increase the count of white blood cells (WBCs) in peripheral blood of leukopenia mice with 15 days treatment. Multi-omics and network pharmacology revealed that leukotriene pathway and MAPK signaling pathway played crucial roles during the treatment of leukopenia with QSC. Six targets (ALOX5, LTB4R, CYSLTR1, FOS, JUN, IL-1β) and 13 prototype compounds were supposed to be the key targets and potential active components, respectively. The validation experiment further confirmed that QSC could effectively inhibit the inflammatory response induced by leukopenia. The inhibitors of ALOX5 activity can significantly increase the number of WBCs in leukopenia mice. Molecular docking of ALOX5 suggested that calycosin, daidzein, and medicarpin were the potentially active compounds of QSC.

CONCLUSION

Leukotriene pathway was found for the first time to be a key role in the development of leukopenia, and ALOX5 was conformed as the potential target. QSC may inhibit the inflammatory response and interfere the leukotriene pathway, it is able to improve hematopoiesis and achieve therapeutic effects in the mice with leukopenia.

Comprehensive computational study in the identification of novel potential cholesterol lowering agents targeting proprotein convertase subtilisin/kexin type 9

The enzymatic target proprotein convertase subtilisin/kexin type 9 (PCSK9) is critically involved in the regulation of the lipoprotein metabolism leading to the degradation of low-density lipoprotein receptors (LDLRs) upon binding. Drugs that lower LDL cholesterol (LDL-C) through the inhibition of PCSK9 are useful in the management of hypercholesterolemia which greatly reduces the associated risk of atherosclerotic cardiovascular disease (CVD). In 2015, anti-PCSK9 monoclonal antibodies (mAbs), alirocumab and evolocumab were approved but owing to their high costs their prior authorization practices were impeded, reducing their long-term adherence. This has drawn considerable attention for the development of small-molecule PCSK9 inhibitors. In this research work, novel and diverse molecules with affinity towards PCSK9 thereby having ability to lower cholesterol. A hierarchical multistep docking was implemented to identify small molecules from chemical libraries with a score cutoff -8.00 kcal/mol, thereby weeding all the non-potential molecules. A set of seven representative molecules Z1139749023, Z1142698190, Z2242867634, Z2242893449, Z2242894417, Z2242909019, and Z2242914794 have been identified from a comprehensive computational study which included assessment of pharmacokinetics and toxicity profiles and binding interactions along with in-depth analysis of structural dynamics and integrity using prolong molecular dynamics (MD) simulation (in-duplicate). Furthermore the binding affinity of these PCSK9 inhibitory candidates molecules was ascertained over 1000 trajectory frames using MM-GBSA calculations. The molecules reported herein are propitious candidates for further development through necessary experimental considerations.Communicated by Ramaswamy H. Sarma.

Exploring the functionality and conservation of Alba proteins in Trypanosoma cruzi: A focus on biological diversity and RNA binding ability

Trypanosoma cruzi is the causative agent of Chagas disease, as well as a trypanosomatid parasite with a complex biological cycle that requires precise mechanisms for regulating gene expression. In Trypanosomatidae, gene regulation occurs mainly at the mRNA level through the recognition of cis elements by RNA-binding proteins (RBPs). Alba family members are ubiquitous DNA/RNA-binding proteins with representatives in trypanosomatid parasites functionally related to gene expression regulation. Although T. cruzi possesses two groups of Alba proteins (Alba1/2 and Alba30/40), their functional role remains poorly understood. Thus, herein, a characterization of T. cruzi Alba (TcAlba) proteins was undertaken. Physicochemical, structural, and phylogenetic analysis of TcAlba showed features compatible with RBPs, such as hydrophilicity, RBP domains/motifs, and evolutionary conservation of the Alba-domain, mainly regarding other trypanosomatid Alba. However, in silico RNA interaction analysis of T. cruzi Alba proteins showed that TcAlba30/40 proteins, but not TcAlba1/2, would directly interact with the assayed RNA molecules, suggesting that these two groups of TcAlba proteins have different targets. Given the marked differences existing between both T. cruzi Alba groups (TcAlba1/2 and TcAlba30/40), regarding sequence divergence, RNA binding potential, and life-cycle expression patterns, we suggest that they would be involved in different biological processes.

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins

Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA binding, it is important to identify which residues in the protein sequence bind to DNA and RNA. At present, there are few methods for specifically identifying the binding sites of disease-related protein-DNA and protein-RNA. In this study, so we combined four machine learning algorithms into an ensemble classifier (EPDRNA) to predict DNA and RNA binding sites in disease-related proteins. The dataset used in model was collated from UniProt and PDB database, and PSSM, physicochemical properties and amino acid type were used as features. The EPDRNA adopted soft voting and achieved the best AUC value of 0.73 at the DNA binding sites, and the best AUC value of 0.71 at the RNA binding sites in 10-fold cross validation in the training sets. In order to further verify the performance of the model, we assessed EPDRNA for the prediction of DNA-binding sites and the prediction of RNA-binding sites on the independent test dataset. The EPDRNA achieved 85% recall rate and 25% precision on the protein-DNA interaction independent test set, and achieved 82% recall rate and 27% precision on the protein-RNA interaction independent test set. The online EPDRNA webserver is freely available at http://www.s-bioinformatics.cn/epdrna .

Potential immune-related therapeutic mechanisms of multiple traditional Chinese medicines on type 2 diabetic nephropathy based on bioinformatics, network pharmacology and molecular docking

BACKGROUND

The prevalence of type 2 diabetic nephropathy (T2DN) ranges from 20 % to 40 % among individuals with type 2 diabetes. Multiple immune pathways play a pivotal role in the pathogenesis of T2DN. This study aimed to investigate the immunomodulatory effects of active ingredients derived from 14 traditional Chinese medicines (TCMs) on T2DN.

METHODS

By removing batch effect on the GSE30528 and GSE96804 datasets, we employed a combination of weighted gene co-expression network analysis, least absolute shrinkage and selection operator analysis, protein-protein interaction network analysis, and the CIBERSORT algorithm to identify the active ingredients of TCMs as well as potential hub biomarkers associated with immune cells. Functional analysis was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and gene set variation analysis (GSVA). Additionally, molecular docking was employed to evaluate interactions between active ingredients and potential immunotherapy targets.

RESULTS

A total of 638 differentially expressed genes (DEGs) were identified in this study, comprising 5 hub genes along with 4 potential biomarkers. Notably, CXCR1, CXCR2, and FOS exhibit significant associations with immune cells while displaying robust or favorable affinities towards the active ingredients kaempferol, quercetin, and luteolin. Furthermore, functional analysis unveiled intricate involvement of DEGs, hub genes and potential biomarkers in pathways closely linked to immunity and diabetes.

CONCLUSION

The potential hub biomarkers and immunotherapy targets associated with immune cells of T2DN comprise CXCR1, CXCR2, and FOS. Furthermore, kaempferol, quercetin, and luteolin demonstrate potential immunomodulatory effects in modulating T2DN through the regulation of CXCR1, CXCR2, and FOS expression.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Gentiopicroside ameliorates glucose and lipid metabolism in T2DM via targeting FGFR1

BACKGROUND

The suppression of the fibroblast growth factor 21/fibroblast growth factor receptor 1 (FGF21/FGFR1) signaling pathway is considered as a vital factor in the type 2 diabetes mellitus (T2DM) progression. Our previous study showed that gentiopicroside (GPS), the main active compound present in Gentiana macrophylla Pall., has the capacity to control disorders related to glucose and lipid metabolism in individuals with T2DM. Nevertheless, the specific mechanism remains unclear.

PURPOSE

In light of the fact that the PharmMapper database suggests FGFR1 as the target of GPS, our investigation aims to determine if GPS can enhance glucose and lipid metabolism issues in T2DM by modulating the FGF21/FGFR1 signaling pathway.

METHODS

In this study, we used palmitic acid (PA)-induced HepG2 cells and db/db mice to investigate the function and mechanism of GPS in the FGF21/FGFR1 signaling pathway. To examine the interaction between GPS and FGFR1, researchers performed Cellular Thermal Shift Assay (CETSA) and Surface Plasmon Resonance (SPR) analysis.

RESULTS

The results suggest that GPS activates the traditional metabolic pathways, including PI3K/AKT and AMPK, which are the subsequent stages of the FGF21/FGFR1 pathway. This activation leads to the enhancement of glucose and lipid metabolism issues in PA-treated HepG2 cells and db/db mice. Furthermore, the depletion of FGFR1 has been noticed to oppose the stimulation of PI3K/AKT and AMPK pathways by GPS in HepG2 cells subjected to PA. Notability, our research affirms that GPS binds directly to FGFR1, hindering the ubiquitinated degradation of FGFR1 by neural precursor cells expressing developmentally decreased protein 4 (NEDD4) and ultimately promoting FGF21 signal transduction.

CONCLUSION

This study demonstrates that GPS targeting FGFR1 activates the PI3K/AKT and AMPK pathways, which is an important mechanism for its treatment of T2DM.

Bioinformatics study of the potential therapeutic effects of ginsenoside Rh3 in reversing insulin resistance

Background

In recent years, the incidence of insulin resistance is increasing, and it can cause a variety of Metabolic syndrome. Ginsenosides have been clinically proven to improve fat metabolism and reduce insulin resistance, but their components and mechanism of action are still unclear.

Objective

Ginsenoside, a bioactive compound derived from ginseng, exhibits significant potential in treating obesity, diabetes, and metabolic disorders. Despite evidence supporting its efficacy in ameliorating insulin resistance (IR) in obesity, the specific bioactive components and underlying mechanisms remain obscure. In this study, we endeavored to elucidate the potential molecular targets and pathways influenced by ginsenoside Rh3 (GRh3) to ameliorate IR in liver tissue. We employed a comprehensive approach that integrates system pharmacology and bioinformatics analysis.

Materials and methods

Our methodology involved the identification of candidate targets for GRh3 and the profiling of differentially expressed genes (DEGs) related to IR in individuals with insulin resistance. The coalescence of candidate targets and DEGs facilitated the construction of a "GRh3-targets-disease" network for each tissue type, ultimately yielding 38 shared target genes. Subsequently, we conducted pathway enrichment analysis, established protein-protein interaction (PPI) networks, and identified hub targets among the GRh3 targets and IR-related DEGs. Additionally, we conducted animal experiments to corroborate the role of these hub targets in the context of GRh3.

Results

Our investigation identified a total of 38 overlapping targets as potential candidates. Notably, our analysis revealed crucial hub targets such as EGFR, SRC, ESR1, MAPK1, and CASP3, alongside implicated signaling pathways, including those related to insulin resistance, the FoxO signaling pathway, the PPAR signaling pathway, and the IL-17 signaling pathway. This study establishes a robust foundation for the mechanisms underlying GRh3's efficacy in mitigating IR. Furthermore, these results suggest that GRh3 may serve as a representative compound within the ginsenoside family.

Conclusion

This study elucidates the potential molecular targets and associated pathways through which GRh3 ameliorates IR, showcasing its multifaceted nature, spanning multiple targets, pathways, and mechanisms. These findings establish a robust foundation for subsequent experimental inquiries and clinical applications.

Microbial Biofilm Inhibition Using Magnetic Cross-Linked Polyphenol Oxidase Aggregates

Microbial biofilm accumulation poses a serious threat to the environment, presents significant challenges to different industries, and exhibits a large impact on public health. Since there has not been a conclusive answer found despite various efforts, the potential green and economical methods are being focused on, particularly the innovative approaches that employ biochemical agents. In the present study, we propose a bio-nanotechnological method using magnetic cross-linked polyphenol oxidase aggregates (PPO m-CLEA) for inhibition of microbial biofilm including multidrug resistant bacteria. Free PPO solution showed only 55-60% biofilm inhibition, whereas m-CLEA showed 70-75% inhibition, as confirmed through microscopic techniques. The carbohydrate and protein contents in biofilm extracellular polymeric substances (EPSs) were reduced significantly. The m-CLEA demonstrated reusability up to 5 cycles with consistent efficiency in biofilm inhibition. Computational work was also done where molecular docking of PPO with microbial proteins associated with biofilm formation was conducted, resulting in favorable binding scores and inter-residual interactions. Overall, both in vitro and in silico results suggest that PPO interferes with microbial cell attachment and EPS formation, thereby preventing biofilm colonization.

Controlling nanoparticle-induced endothelial leakiness with the protein corona

Understanding nanoparticle-cell interaction is essential for advancing research in nanomedicine and nanotoxicology. Apart from the transcytotic pathway mediated by cellular recognition and energetics, nanoparticles (including nanomedicines) may harness the paracellular route for their transport by inducing endothelial leakiness at cadherin junctions. This phenomenon, termed as NanoEL, is correlated with the physicochemical properties of the nanoparticles in close association with cellular signalling, membrane mechanics, as well as cytoskeletal remodelling. However, nanoparticles in biological systems are transformed by the ubiquitous protein corona and yet the potential effect of the protein corona on NanoEL remains unclear. Using confocal fluorescence microscopy, biolayer interferometry, transwell, toxicity, and molecular inhibition assays, complemented by molecular docking, here we reveal the minimal to significant effects of the anionic human serum albumin and fibrinogen, the charge neutral immunoglobulin G as well as the cationic lysozyme on negating gold nanoparticle-induced endothelial leakiness in vitro and in vivo. This study suggests that nanoparticle-cadherin interaction and hence the extent of NanoEL may be partially controlled by pre-exposing the nanoparticles to plasma proteins of specific charge and topology to facilitate their biomedical applications.

Geniposide effectively safeguards HT22 cells against Aβ-induced damage by activating mitophagy via the PINK1/Parkin signaling pathway

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the significant involvement of amyloid-beta (Aβ) peptide in its pathogenesis. Geniposide, derived from the versatile medicinal of Gardenia jasminoides, is one of the active compounds studied extensively. The objective was to explore the impact of geniposide on Aβ25-35-induced damage in HT22 cells, specifically focusing on its modulation of PINK1/Parkin-mediated mitophagy. In our investigation, geniposide exhibited remarkable restorative effects by enhancing cell viability and preserving the mitochondrial membrane potential. Moreover, it effectively reduced and mitigated the oxidative stress and apoptosis rates induced by Aβ25-35. Notably, geniposide exhibited the capacity to enhance autophagic flux, upregulate LC3II and Beclin-1 expression, and downregulate the expression of p62. Furthermore, geniposide positively influenced the expression of PINK1 and Parkin proteins, with molecular docking substantiating a strong interaction between geniposide and PINK1/Parkin proteins. Intriguingly, the beneficial outcomes of geniposide on alleviating the pronounced apoptosis rates, the overproduction of reactive oxygen species, and diminished the PINK1 and Parkin expression induced by Aβ25-35 were compromised by the mitophagy inhibitor cyclosporine A (CsA). Collectively, these findings suggested that geniposide potentially shields HT22 cells against neurodegenerative damage triggered by Aβ25-35 through the activation of mitophagy. The insights contribute valuable references to the defensive consequences against neurological damage of geniposide, thereby highlighting its potential as a therapeutic intervention in AD.

Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases

Astrocytes play a pivotal role in maintaining brain homeostasis. Recent research has highlighted the significance of palmitic acid (PA) in triggering pro-inflammatory pathways contributing to neurotoxicity. Furthermore, Genomic-scale metabolic models and control theory have revealed that metabolic switches (MSs) are metabolic pathway regulators by potentially exacerbating neurotoxicity, thereby offering promising therapeutic targets. Herein, we characterized these enzymatic MSs in silico as potential therapeutic targets, employing protein-protein and drug-protein interaction networks alongside structural characterization techniques. Our findings indicate that five MSs (P00558, P04406, Q08426, P09110, and O76062) were functionally linked to nervous system drug targets and may be indirectly regulated by specific neurological drugs, some of which exhibit polypharmacological potential (e.g., Trifluperidol, Trifluoperazine, Disulfiram, and Haloperidol). Furthermore, four MSs (P00558, P04406, Q08426, and P09110) feature ligand-binding or allosteric cavities with druggable potential. Our results advocate for a focused exploration of P00558 (phosphoglycerate kinase 1), P04406 (glyceraldehyde-3-phosphate dehydrogenase), Q08426 (peroxisomal bifunctional enzyme, enoyl-CoA hydratase, and 3-hydroxyacyl CoA dehydrogenase), P09110 (peroxisomal 3-ketoacyl-CoA thiolase), and O76062 (Delta(14)-sterol reductase) as promising targets for the development or repurposing of pharmacological compounds, which could have the potential to modulate lipotoxic-altered metabolic pathways, offering new avenues for the treatment of related human diseases such as neurological diseases.

Exploring the molecular mechanism of Epimedium for the treatment of ankylosing spondylitis based on network pharmacology, molecular docking, and molecular dynamics simulations

Ankylosing spondylitis (AS) is a rheumatic disease that causes inflammation and bone formation in the spine. Despite significant advances in treatment, adverse side effects have triggered research into natural compounds. Epimedium (EP) is a traditional Chinese herb with a variety of pharmacological activities, including antirheumatic, anti-inflammatory, and immunomodulatory activities; however, its direct effects on AS treatment and the underlying molecular mechanisms have not been systematically studied. Thus, here, we used network pharmacology, molecular docking, and molecular dynamics simulations to explore the targets of EP for treating AS. We constructed an interaction network to elucidate the complex relationship between EP and AS. Sixteen active ingredients in EP were screened; 80 potential targets were identified. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone, anhydroicaritin, and luteolin were the core components and TNF, IL-6, IL-1β, MMP9, and PTGS2 were the core targets. The GO and KEGG analyses indicated that EP may modulate multiple biological processes and pathways, including the AGE-RAGE, TNF, NF-κB/MAPK, and TLR signaling pathways, for AS treatment. Molecular docking and molecular dynamics simulations showed good affinity between the active components and core targets of EP, with stable binding within 100 nanoseconds. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone possessed the highest free energy of binding to PTGS2 and TNF (-115.575 and - 87.676 kcal/mol, respectively). Thus, EP may affect AS through multiple pathways, including the alleviation of inflammation, oxidative stress, and immune responses. In summary, we identified the active components and potential targets of EP, highlighting new strategies for the further experimental validation and exploration of lead compounds for treating AS.

Delineating organizational principles of the endogenous L-A virus by cryo-EM and computational analysis of native cell extracts

The high abundance of most viruses in infected host cells benefits their structural characterization. However, endogenous viruses are present in low copy numbers and are therefore challenging to investigate. Here, we retrieve cell extracts enriched with an endogenous virus, the yeast L-A virus. The determined cryo-EM structure discloses capsid-stabilizing cation-π stacking, widespread across viruses and within the Totiviridae, and an interplay of non-covalent interactions from ten distinct capsomere interfaces. The capsid-embedded mRNA decapping active site trench is supported by a constricting movement of two flexible opposite-facing loops. tRNA-loaded polysomes and other biomacromolecules, presumably mRNA, are found in virus proximity within the cell extract. Mature viruses participate in larger viral communities resembling their rare in-cell equivalents in terms of size, composition, and inter-virus distances. Our results collectively describe a 3D-architecture of a viral milieu, opening the door to cell-extract-based high-resolution structural virology.

MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search

Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI's nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.

FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval

The release of AlphaFold2 has sparked a rapid expansion in protein model databases. Efficient protein structure retrieval is crucial for the analysis of structure models, while measuring the similarity between structures is the key challenge in structural retrieval. Although existing structure alignment algorithms can address this challenge, they are often time-consuming. Currently, the state-of-the-art approach involves converting protein structures into three-dimensional (3D) Zernike descriptors and assessing similarity using Euclidean distance. However, the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based, thus limiting their application in studying custom datasets. To overcome this limitation, we developed FP-Zernike, a user-friendly toolkit for computing different types of Zernike descriptors based on feature points. Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets. FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets. In addition, we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank (PDB) dataset to facilitate the local deployment of this tool for interested readers. Our demonstration contained 590,685 structures, and at this scale, our system required only 4-9 s to complete a retrieval. The experiments confirmed that it achieved the state-of-the-art accuracy level. FP-Zernike is an open-source toolkit, with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1, as well as through a webserver at http://www.structbioinfo.cn/.

Target prediction and potential application of dihydroartemisinin on hepatocarcinoma treatment

With high incidence of hepatocarcinoma and limited effective treatments, most patients suffer in pain. Antitumor drugs are single-targeted, toxicity, causing adverse side effects and resistance. Dihydroartemisinin (DHA) inhibits tumor through multiple mechanisms effectively. This study explores and evaluates safety and potential mechanism of DHA towards human hepatocarcinoma based on network pharmacology in a comprehensive way. Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of DHA were evaluated with pkCSM, SwissADME, and ADMETlab. Potential targets of DHA were obtained from SwissTargetPrediction, Drugbank, TargetNET, and PharmMapper. Target gene of hepatocarcinoma was obtained from OMIM, GeneCards, and DisGeNET. Overlapping targets and hub genes were identified and analyzed for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway. Molecular docking was utilized to investigate the interactions sites and hydrogen bonds. Cell counting kit-8 (CCK8), wound healing, invasion, and migration assays on HepG2 and SNU387 cell proved DHA inhibits malignant biological features of hepatocarcinoma cell. DHA is safe and desirable for clinical application. A total of 131 overlapping targets were identified. Biofunction analysis showed targets were involved in kinase activity, protein phosphorylation, intracellular reception, signal transduction, transcriptome dysregulation, PPAR pathway, and JAK-STAT signaling axis. Top 9 hub genes were obtained using MCC (Maximal Clique Centrality) algorithm, namely CDK1, CCNA2, CCNB1, CCNB2, KIF11, CHEK1, TYMS, AURKA, and TOP2A. Molecular docking suggests that all hub genes form a stable interaction with DHA for optimal binding energy were all less than - 5 kcal/mol. Dihydroartemisinin might be a potent and safe anticarcinogen based on its biological safety and effective therapeutic effect.

TransGEM: a molecule generation model based on Transformer with gene expression data

MOTIVATION

It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information.

RESULTS

We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/hzauzqy/TransGEM.

Mechanisms of inhibition of advanced glycation end-products (AGEs) and α-glucosidase by Heliotropium bacciferum: Spectroscopic and molecular docking analysis

Diabetes mellitus is characterized by hyperglycemia that makes insulin more prone to glycation and form advanced glycation end products (AGEs). Here, we report the effect of glyoxal (GO) on the formation of AGEs using human insulin as model protein and their structural modifications. The present investigation also reports the anti-AGE potential of Heliotropium bacciferum (Leaf) extracts. The phytochemical analysis of H. bacciferum revealed that free phenolic extract contains higher amount of total phenolic (3901.58 ± 17.06 mg GAE/100 g) and total flavonoid content (30.41 ± 0.32 mg QE/100 g) when compared to bound phenolic extract. Naringin and caffeic acid were identified as the major phenolic ingredients by UPLC-PAD method. Furthermore, bound phenolics extract showed significantly higher DPPH and superoxide radicals scavenging activity (IC50 17.53 ± 0.36 μg/mL and 0.306 ± 0.038 mg/ mL, respectively) (p ≤ 0.05). Besides, the bound phenolics extract also showed significant (p ≤ 0.05) chelating power (IC50 0.063) compared to free phenolic extract. In addition, bound phenolic extract could efficiently trap GO under physiological conditions. Spectroscopic investigation of GO-modified insulin illustrated changes in the tertiary structure of insulin and formation of AGEs. On the other hand, no significant alteration in secondary structure was observed by far UV-CD measurement. Furthermore, H. bacciferum extract inhibited α-glucosidase activity and AGEs formation implicated in diabetes. Molecular docking analysis depicted that GO bind with human insulin in both chains and forms a stable complex with TYR A: 14, LEU A:13, ASN B:3, SER A:12 amino acid residues with binding energy of - 2.53 kcal/mol. However, caffeic acid binds to ASN A:18 and GLU A:17 residues of insulin with lower binding energy of -4.67 kcal/mol, suggesting its higher affinity towards human insulin compared to GO. Our finding showed promising activity of H. bacciferum against AGEs and its complications. The major phenolics like caffeic acid, naringin and their derivatives could be exploited for the drug development for management of AGEs in diabetes.

Molecular and Structural Insights into Buffalo Interleukin-17A

Interleukin-17A is a pro-inflammatory cytokine that plays a key role in the immune response to many pathogens and implicated in autoimmune diseases. This molecule is also involved in providing protection to many bacterial and fungal infections of gastro-intestinal tract and respiratory mucosa. Although molecular aspect of IL-17A has been studied in few species, no data are available for buffalo, which is one of the major sources of milk production in India. Therefore, in the present study, IL-17A gene of Indian Murrah Buffalo origin was cloned, expressed, and analyzed using bioinformatic tools. The coding sequence of buffalo IL-17A gene was cloned in prokaryotic expression vector (pET-28a) followed by its expression, purification, and characterization. A computational analysis was performed to understand the sequence, structure, and evolutionary relationship of buIL-17A. It revealed that the length of buIL-17A sequence without signal peptide is 132 amino acids as in cattle. However, sequence identity is found to be 99% due to one amino substitution difference between buffalo and cattle. After analysis, it can be concluded that buIL-17A recombinant protein can be used as a potential immunobiological reagent for diagnostic and therapeutic purpose.

Exploring the mechanism of action of the combination of cinnamon and motherwort in the treatment of benign prostatic hyperplasia: A network pharmacology study

Cinnamon and motherwort are traditional Chinese medicines and are often combined to treat benign prostatic hyperplasia; however, the specific therapeutic mechanisms involved remain unclear. Therefore, in this study, we applied a network pharmacology approach to investigate the potential mechanisms of action of the drug pair cinnamon and motherwort (PCM) for the treatment of benign prostatic hyperplasia. Relevant targets for the use of PCM to treat benign prostatic hyperplasia were obtained through databases. Protein-protein interactions were then identified by the STRING database and core targets were screened. Enrichment analysis was conducted through the Metascape platform. Finally, molecular docking experiments were carried out to evaluate the affinity between the target proteins and ligands of PCM. We identified 22 active ingredients in PCM, 315 corresponding targets and 130 effective targets of PCM for the treatment of benign prostatic hyperplasia. These targets were related to the PI3K-Akt, MAPK, FoxO, TNF, and IL-17 signaling pathways. Network pharmacology was used to identify the effective components and action targets of PCM. We also identified potential mechanisms of action for PCM in the treatment of benign prostatic hyperplasia. Our results provide a foundation for expanding the clinical application of PCM and provide new ideas and directions for further research on the mechanisms of action of PCM and its components for the treatment of benign prostatic hyperplasia.

Structure-Based Rational and General Strategy for Stabilizing Single-Chain T-Cell Receptors to Enhance Affinity

The T-cell receptor (TCR) is a crucial molecule in cellular immunity. The single-chain T-cell receptor (scTCR) is a potential format in TCR therapeutics because it eliminates the possibility of αβ-TCR mispairing. However, its poor stability and solubility impede the in vitro study and manufacturing of therapeutic applications. In this study, some conserved structural motifs are identified in variable domains regardless of germlines and species. Theoretical analysis helps to identify those unfavored factors and leads to a general strategy for stabilizing scTCRs by substituting residues at exact IMGT positions with beneficial propensities on the consensus sequence of germlines. Several representative scTCRs are displayed to achieve stability optimization and retain comparable binding affinities with the corresponding αβ-TCRs in the range of μM to pM. These results demonstrate that our strategies for scTCR engineering are capable of providing the affinity-enhanced and specificity-retained format, which are of great value in facilitating the development of TCR-related therapeutics.

Alternate conformations found in protein structures implies biological functions: A case study using cyclophilin A

Protein dynamics linked to numerous biomolecular functions, such as ligand binding, allosteric regulation, and catalysis, must be better understood at the atomic level. Reactive atoms of key residues drive a repertoire of biomolecular functions by flipping between alternate conformations or conformational substates, seldom found in protein structures. Probing such sparsely sampled alternate conformations would provide mechanistic insight into many biological functions. We are therefore interested in evaluating the instance of amino acids adopted alternate conformations, either in backbone or side-chain atoms or in both. Accordingly, over 70000 protein structures appear to contain alternate conformations only 'A' and 'B' for any atom, particularly the instance of amino acids that adopted alternate conformations are more for Arg, Cys, Met, and Ser than others. The resulting protein structure analysis depicts that amino acids with alternate conformations are mainly found in the helical and β-regions and are often seen in high-resolution X-ray crystal structures. Furthermore, a case study on human cyclophilin A (CypA) was performed to explain the pre-existing intrinsic dynamics of catalytically critical residues from the CypA and how such intrinsic dynamics perturbed upon Ser99Thr mutation using molecular dynamics simulations on the ns-μs timescale. Simulation results demonstrated that the Ser99Thr mutation had impaired the alternate conformations or the catalytically productive micro-environment of Phe113, mimicking the experimentally observed perturbation captured by X-ray crystallography. In brief, a deeper comprehension of alternate conformations adopted by the amino acids may shed light on the interplay between protein structure, dynamics, and function.

Novel PET Imaging Probe for Quantitative Detection of Senescence In Vivo

Real-time detection of cellular senescence remains a clinical challenge. Here, we aimed to develop a positron emission tomography (PET) imaging probe targeting senescence-associated β-galactosidase (SA-β-Gal), the most widely used biomarker of cellular senescence, and investigate its performance for real-time in vivo quantitative detection of cellular senescence. A stable PET imaging agent [68Ga]Ga-BGal was obtained with a high labeling yield (90.0 ± 4.3%) and a radiochemical purity (>95%). [68Ga]Ga-BGal displayed high sensitivity and specificity for β-Gal both in vitro and in vivo. The reaction and uptake of the probe correlated with the β-Gal concentration and reaction time. In PET imaging, high β-Gal-expressing CT26.CL25 tumors and doxorubicin-treated HeLa tumors showed high signals from [68Ga]Ga-BGal, while a low signal was observed in CT26.WT and untreated HeLa tumors. In summary, we showcased successful PET imaging of senescence in preclinical models using probe [68Ga]Ga-BGal. This finding holds the potential for translating senescence imaging into clinical applications.

KEGG orthology prediction of bacterial proteins using natural language processing

BACKGROUND

The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights.

RESULTS

In this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively.

CONCLUSIONS

Our experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

Antifungal plant flavonoids identified in silico with potential to control rice blast disease caused by Magnaporthe oryzae

Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a severe threat to rice production, particularly in Asia where rice is a staple food. Concerns over fungicide resistance and environmental impact have sparked interest in exploring natural fungicides as potential alternatives. This study aimed to identify highly potent natural fungicides against M. oryzae to combat rice blast disease, using advanced molecular dynamics techniques. Four key proteins (CATALASE PEROXIDASES 2, HYBRID PKS-NRPS SYNTHETASE TAS1, MANGANESE LIPOXYGENASE, and PRE-MRNA-SPLICING FACTOR CEF1) involved in M. oryzae's infection process were identified. A list of 30 plant metabolites with documented antifungal properties was compiled for evaluation as potential fungicides. Molecular docking studies revealed that 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin exhibited superior binding affinities compared to reference fungicides (Azoxystrobin and Tricyclazole). High throughput molecular dynamics simulations were performed, analyzing parameters like RMSD, RMSF, Rg, SASA, hydrogen bonds, contact analysis, Gibbs free energy, and cluster analysis. The results revealed stable interactions between the selected metabolites and the target proteins, involving important hydrogen bonds and contacts. The SwissADME server analysis indicated that the metabolites possess fungicide properties, making them effective and safe fungicides with low toxicity to the environment and living beings. Additionally, bioactivity assays confirmed their biological activity as nuclear receptor ligands and enzyme inhibitors. Overall, this study offers valuable insights into potential natural fungicides for combating rice blast disease, with 2-Coumaroylquinic acid, Myricetin, Rosmarinic Acid, and Quercetin standing out as promising and environmentally friendly alternatives to conventional fungicides. These findings have significant implications for developing crop protection strategies and enhancing global food security, particularly in rice-dependent regions.

LogoMotif: A Comprehensive Database of Transcription Factor Binding Site Profiles in Actinobacteria

Actinobacteria undergo a complex multicellular life cycle and produce a wide range of specialized metabolites, including the majority of the antibiotics. These biological processes are controlled by intricate regulatory pathways, and to better understand how they are controlled we need to augment our insights into the transcription factor binding sites. Here, we present LogoMotif (https://logomotif.bioinformatics.nl), an open-source database for characterized and predicted transcription factor binding sites in Actinobacteria, along with their cognate position weight matrices and hidden Markov models. Genome-wide predictions of binding site locations in Streptomyces model organisms are supplied and visualized in interactive regulatory networks. In the web interface, users can freely access, download and investigate the underlying data. With this curated collection of actinobacterial regulatory interactions, LogoMotif serves as a basis for binding site predictions, thus providing users with clues on how to elicit the expression of genes of interest and guide genome mining efforts.

RNA oligomers at atomic resolution containing 1-methylpseudouridine, an essential building block of mRNA vaccines

All widely used mRNA vaccines against COVID-19 contain in their sequence 1-methylpseudouridine (m1Ψ) instead of uridine. In this publication, we report two high resolution crystal structures (at up to 1.01 and 1.32 Å, respectively) of one such double-stranded 12-mer RNA sequence crystallized in two crystal forms. The structures are compared with similar structures which do not contain this modification. Additionally, the X-ray structure of 1-methyl-pseudouridine itself was determined.