Topological and system-level protein interaction network (PIN) analyses to deduce molecular mechanism of curcumin

Exploring the effect and mechanism of Hippophae rhamnoides L. triterpenoid acids on improving NAFLD based on network pharmacology and experimental validation in vivo and in vitro

ETHNOPHARMACOLOGICAL RELEVANCE

Sea buckthorn (Hippophae rhamnoides L.) is a traditional Chinese medicinal and possesses a rich medical history in terms of treating gastric disorders, sputum and cough and liver injuries in oriental medicinal system. By reason of the complicated chemical constituents, the material basis and potential pharmacological mechanism of sea buckthorn acting on Non-alcoholic fatty liver disease (NAFLD) has not been clearly elucidated.

AIM OF THE STUDY

To explore the pharmacological efficacy and underlying mechanism of sea buckthorn triterpenoid acid enrichment (STE) in the treatment of NAFLD.

MATERIALS AND METHODS

The approaches of Network pharmacology and experiment validation in vitro and in vivo were applied in this study. Firstly, targets of triterpenoid acid compounds and NAFLD were collected from databases. The crucial targets were screened by the construction of protein-protein interaction (PPI) network. Furthermore, the potential signaling pathways and targets affected by STE was predicted by GO together with KEGG enrichment analysis. Finally, the experiment validation was carried out through high-fat feeding NAFLD mice and lipid accumulation HepG2 cell model. Lipids and liver related biochemical indicators were determined, Oil Red O and H&E staining were employed to observe fat accumulation. In addition, the expression levels of proteins of key target and signal pathway anticipated in network pharmacology were detected to elaborated its action mechanism.

RESULTS

A total of 180 intersecting potential targets for enhancing NAFLD with STE were eventually identified. 6 key targets including AKT1, TNF, IL6, INS, JUN, STAT3 and TP53 were further identified and the AMPK-SREBP1 pathway was enriched. Animal experiment result showed that STE treatment could significantly reduce the levels of TG, TC, LDL-C, ALT and AST, increase the levels of HDL-C in serum, and improve lipid accumulation of epididymal fat and liver. The results of the lipid accumulation cell model indicated that STE and key compound oleanolic acid could diminish intracellular lipid levels of TG, TC, LDL-C and number of lipid droplets. Western blot results showed that the above beneficial effects could be achieved by regulating the expression of p-AMPK/AMPK, SREBP1, FAS, ACC, SCD protein.

CONCLUSION

This study confirmed the effect of STE on improving NAFLD and the potential action mechanism was involved in the regulation of the AMPK-SREBP1 pathway.

Exosomes derived from tumor adjacent fibroblasts efficiently target pancreatic tumors

The application of extracellular vesicles, particularly exosomes (EXs), is rapidly expanding in the field of medicine, owing to their remarkable properties as natural carriers of biological cargo. This study investigates utilization of exosomes derived from stromal cells of tumor adjacent normal tissues (NAF-EXs) for personalized medicine, which can be derived at the time of diagnosis by endoscopic ultrasound. Herein, we show that exosomes (EXs) derived from NAFs demonstrate differential bio-physical characteristics, efficient cellular internalization, drug loading efficiency, pancreatic tumor targeting and delivery of payloads. NAF-derived EXs (NAF-EXs) were used for loading ormeloxifene (ORM), a potent anti-cancer and desmoplasia inhibitor as a model drug. We found that ORM maintains normal fibroblast cell phenotype and renders them incompatible to be triggered for a CAF-like phenotype, which may be due to regulation of Ca2+ influx in fibroblast cells. NAF-EXs-ORM effectively blocked oncogenic signaling pathways involved in desmoplasia and epithelial mesenchymal transition (EMT) and repressed tumor growth in xenograft mouse model. In conclusion, our data suggests preferential tropism of NAF-EXs for PDAC tumors, thus imply feasibility of developing a novel personalized medicine for PDAC patients using autologous NAF-EXs for improved therapeutic outcome of anti-cancer drugs. Additionally, it provides the opportunity of utilizing this biological scaffold for effective therapeutics in combination with standard therapeutic regimen.

Role of molecular mimicry in the SARS-CoV-2-human interactome for pathogenesis of cardiovascular diseases: An update to ImitateDB

Mimicry of host proteins is a strategy employed by pathogens to hijack host functions. Domain and motif mimicry was explored in the experimental and predicted SARS-CoV-2-human interactome. The host first interactor proteins were also added to capture the continuum of the interactions. The domains and motifs of the proteins were annotated using NCBI CD Search and ScanProsite, respectively. Host and pathogen proteins with a common host interactor and similar domain/motif constitute a mimicry pair indicating global structural similarity (domain mimicry pair; DMP) or local sequence similarity (motif mimicry pair; MMP). 593 DMPs and 7,02,472 MMPs were determined. AAA, DEXDc and Macro domains were frequent among DMPs whereas glycosylation, myristoylation and RGD motifs were abundant among MMP. The proteins involved in mimicry were visualised as a SARS-CoV-2 mimicry interaction network. The host proteins were enriched in multiple CVD pathways indicating the role of mimicry in COVID-19 associated CVDs. Bridging nodes were identified as potential drug targets. Approved antihypertensive and anti-inflammatory drugs are proposed for repurposing against COVID-19 associated CVDs. The SARS-CoV-2 mimicry data has been updated in ImitateDB (http://imitatedb.sblab-nsit.net/SARSCoV2Mimicry). Determination of key mechanisms, proteins, pathways, drug targets and repurposing candidates is critical for developing therapeutics for SARS CoV-2 associated CVDs.

Integrative big transcriptomics data analysis implicates crucial role of MUC13 in pancreatic cancer

Big data analysis holds a considerable influence on several aspects of biomedical health science. It permits healthcare providers to gain insights from large and complex datasets, leading to improvements in the understanding, diagnosis, medication, and restraint of pathological conditions including cancer. The incidences of pancreatic cancer (PanCa) are sharply rising, and it will become the second leading cause of cancer related deaths by 2030. Various traditional biomarkers are currently in use but are not optimal in sensitivity and specificity. Herein, we determine the role of a new transmembrane glycoprotein, MUC13, as a potential biomarker of pancreatic ductal adenocarcinoma (PDAC) by using integrative big data mining and transcriptomic approaches. This study is helpful to identify and appropriately segment the data related to MUC13, which are scattered in various data sets. The assembling of the meaningful data, representation strategy was used to investigate the MUC13 associated information for the better understanding regarding its structural, expression profiling, genomic variants, phosphorylation motifs, and functional enrichment pathways. For further in-depth investigation, we have adopted several popular transcriptomic methods like DEGseq2, coding and non-coding transcript, single cell seq analysis, and functional enrichment analysis. All these analyzes suggest the presence of three nonsense MUC13 genomic transcripts, two protein transcripts, short MUC13 (s-MUC13, non-tumorigenic or ntMUC13), and long MUC13 (L-MUC13, tumorigenic or tMUC13), several important phosphorylation sites in tMUC13. Altogether, this data confirms that importance of tMUC13 as a potential biomarker, therapeutic target of PanCa, and its significance in pancreatic pathobiology.

Glutathione-Responsive Tannic Acid-Assisted FRET Nanomedicine for Cancer Therapy

In cancer combination therapy, a multimodal delivery vector is used to improve the bioavailability of multiple anti-cancer hydrophobic drugs. Further, targeted delivery of therapeutics along with simultaneous monitoring of the drug release at the tumor site without normal organ toxicity is an emerging and effective strategy for cancer treatment. However, the lack of a smart nano-delivery system limits the application of this therapeutic strategy. To overcome this issue, a PEGylated dual drug, conjugated amphiphilic polymer (CPT-S-S-PEG-CUR), has been successfully synthesized by conjugating two hydrophobic fluorescent anti-cancer drugs, curcumin (CUR) and camptothecin (CPT), through an ester and a redox-sensitive disulfide (-S-S-) linkage, respectively, with a PEG chain via in situ two-step reactions. CPT-S-S-PEG-CUR is spontaneously self-assembled in the presence of tannic acid (TA, a physical crosslinker) into anionic, comparatively smaller-sized (~100 nm), stable nano-assemblies in water in comparison to only polymer due to stronger H-bond formation between polymer and TA. Further, due to the spectral overlap between CPT and CUR and a stable, smaller nano-assembly formation by the pro-drug polymer in water in presence of TA, a successful Fluorescence Resonance Energy Transfer (FRET) signal was generated between the conjugated CPT (FRET donor) and conjugated CUR (FRET acceptor). Interestingly, these stable nano-assemblies showed a preferential breakdown and release of CPT in a tumor-relevant redox environment (in the presence of 50 mM glutathione), leading to the disappearance of the FRET signal. These nano-assemblies exhibited a successful cellular uptake by the cancer cells and an enhanced antiproliferative effect in comparison to the individual drugs in cancer cells (AsPC1 and SW480). Such promising in vitro results with a novel redox-responsive, dual-drug conjugated, FRET pair-based nanosized multimodal delivery vector can be highly useful as an advanced theranostic system towards effective cancer treatment.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Systematic analysis to identify novel disease indications and plausible potential chemical leads of glutamate ionotropic receptor NMDA type subunit 1, GRIN1

Schizophrenia is a mental illness affecting the normal lifestyle of adults and early adolescents incurring major symptoms as jumbled speech, involvement in everyday activities eventually got reduced, patients always struggle with attention and memory, reason being both the genetic and environmental factors responsible for altered brain chemistry and structure, resulting in schizophrenia and associated orphan diseases. The network biology describes the interactions among genes/proteins encoding molecular mechanisms of biological processes, development, and diseases. Besides, all the molecular networks, protein-protein Interaction Networks have been significant in distinguishing the pathogenesis of diseases and thereby drug discovery. The present meta-analysis prioritizes novel disease indications viz. rare and orphan diseases associated with target Glutamate Ionotropic Receptor NMDA Type Subunit 1, GRIN1 using text mining knowledge-based tools. Furthermore, ZINC database was virtually screened, and binding conformation of selected compounds was performed and resulted in the identification of Narciclasine (ZINC04097652) and Alvespimycin (ZINC73138787) as potential inhibitors. Furthermore, docked complexes were subjected to MD simulation studies which suggests that the identified leads could be a better potential drug to recuperate schizophrenia.

Deciphering Spatial Protein-Protein Interactions in Brain Using Proximity Labeling

Cellular biomolecular complexes including protein-protein, protein-RNA, and protein-DNA interactions regulate and execute most biological functions. In particular in brain, protein-protein interactions (PPIs) mediate or regulate virtually all nerve cell functions, such as neurotransmission, cell-cell communication, neurogenesis, synaptogenesis, and synaptic plasticity. Perturbations of PPIs in specific subsets of neurons and glia are thought to underly a majority of neurobiological disorders. Therefore, understanding biological functions at a cellular level requires a reasonably complete catalog of all physical interactions between proteins. An enzyme-catalyzed method to biotinylate proximal interacting proteins within 10 to 300 nm of each other is being increasingly used to characterize the spatiotemporal features of complex PPIs in brain. Thus, proximity labeling has emerged recently as a powerful tool to identify proteomes in distinct cell types in brain as well as proteomes and PPIs in structures difficult to isolate, such as the synaptic cleft, axonal projections, or astrocyte-neuron junctions. In this review, we summarize recent advances in proximity labeling methods and their application to neurobiology.

Analysis of the multi-physiological and functional mechanism of wheat alkylresorcinols based on reverse molecular docking and network pharmacology

Alkylresorcinols (ARs) are phenolic lipids present in the bran part of whole grain wheat and rye, which possess antioxidant, anti-inflammatory, anti-cancer and anti-tumor properties. The physiological activities of ARs have been proven to be diverse; however, the specific molecular mechanisms are still unclear. In this study, reverse virtual screening and network pharmacology were used to explore the potential molecular mechanisms of the physiological function of ARs and their endogenous metabolites. The Metascape database was used for GO enrichment and KEGG pathway analysis. Furthermore, molecular docking was used to investigate the interactions between active compounds and potential targets. The results showed that the bioavailability of most ARs and their endogenous metabolites was 0.55 and 0.56, while the bioavailability of certain endogenous metabolites was only 0.11. Multiplex analysis was used to screen 73 important targets and 4 core targets (namely, HSP90AA1, EP300, HSP90AB1 and ERBB2) out of the 163 initial targets. The important targets involved in the key KEGG pathway were pathways in cancer (hsa05200), lipid and atherosclerosis (hsa05417), Th17 cell differentiation (hsa04659), chemical carcinogenesis-receptor activation (hsa05207), and prostate cancer (hsa05215). The compounds involved in the core targets were AR-C21, AR-C19, AR-C17, 3,5-DHPHTA-S, 3,5-DHPHTA-G, 3,5-DHPPTA, 3,5-DHPPTA-S, 3,5-DHPPTA-G, 3,5-DHPPTA-Gly and 3,5-DHPPA-G. The interaction force between them was mainly related to hydrogen bonds and van der Waals. Overall, the physiological activities of ARs are not only related to their multiple targets, but may also be related to the synergistic effect of their endogenous metabolites.

Tannic Acid Exhibits Antiangiogenesis Activity in Nonsmall-Cell Lung Cancer Cells

Nonsmall-cell lung cancer (NSCLC) is the most common type of lung cancer, with a dismal prognosis. NSCLC is a highly vascularized tumor, and chemotherapy is often hampered by the development of angiogenesis. Therefore, suppression of angiogenesis is considered a potential treatment approach. Tannic acid (TA), a natural polyphenol, has been demonstrated to have anticancer properties in a variety of cancers; however, its angiogenic properties have yet to be studied. Hence, in the current study, we investigated the antiproliferative and antiangiogenic effects of TA on NSCLC cells. The (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium) (MTS) assay revealed that TA induced a dose- and time-dependent decrease in the proliferation of A549 and H1299 cells. However, TA had no significant toxicity effects on human bronchial epithelial cells. Clonogenicity assay revealed that TA suppressed colony formation ability in NSCLC cells in a dose-dependent manner. The anti-invasiveness and antimigratory potential of TA were confirmed by Matrigel and Boyden chamber studies, respectively. Importantly, TA also decreased the ability of human umbilical vein endothelial cells (HUVEC) to form tube-like networks, demonstrating its antiangiogenic properties. Extracellular vascular endothelial growth factor (VEGF) release was reduced in TA-treated cells compared to that in control cells, as measured by the enzyme-linked immunosorbent assay (ELISA). Overall, these results demonstrate that TA can induce antiproliferative and antiangiogenic effects against NSCLC.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Immunomodulatory and anti-cytokine therapeutic potential of curcumin and its derivatives for treating COVID-19 - a computational modeling

The unavailability of vaccine and medicines raised serious issues during COVID-19 pandemic and peoples from different parts of world relied on traditional medicine for their immediate recovery from COVID-19 and it found effective also. The current research aims to target COVID-19 immunological human host receptors i.e. angiotensin-converting enzyme (ACE)-2, interleukin (IL)-1β, IL-6, tumor necrosis factor-alpha (TNF-α) and protease-activated receptor (PAR)-1 using curcumin derivatives to prevent viral infection and control overproduction of early clinical responses of COVID-19. Targeting these host proteins will mitigate the infection and will filter out many complications caused by these proteins in COVID-19 patients. It is proven through computer-aided computational modeling approaches, total 30 compounds of curcumin and its derivatives were chosen. Drug-likeness parameters were calculated for curcumin and its derivatives and 20 curcumin analogs were selected for docking analysis. From docking analysis of 20 curcumin analogs against five chosen human host receptor targets reveals 11 curcumin analogs possess least binding affinity and best interaction at active sites subjected to absorption, distribution, metabolism, excretion (ADME) analysis. Density functional theory (DFT) analysis of five final shortlisted curcumin derivatives was done to show least binding affinity toward chosen host target protein. Molecular dynamics simulation (MDS) was performed to observe behavior and interaction of potential drug hydrazinocurcumin against target proteins ACE-2 and PAR-1. It was performed at 100 nanoseconds and showed satisfactory results. Finally, our investigation reveals that hydrazinocurcumin possesses immunomodulatory and anti-cytokine therapeutic potential against COVID-19 and it can act as COVID-19 warrior drug molecule and promising choice of drug for COVID-19 treatment, however, it needs further in vivo clinical evaluation to commercialize as COVID-19 drug.Communicated by Ramaswamy H. Sarma.

Role of Nutraceuticals in COVID-19 Mediated Liver Dysfunction

COVID-19 is known as one of the deadliest pandemics of the century. The rapid spread of this deadly virus at incredible speed has stunned the planet and poses a challenge to global scientific and medical communities. Patients with COVID-19 are at an increased risk of co-morbidities associated with liver dysfunction and injury. Moreover, hepatotoxicity induced by antiviral therapy is gaining importance and is an area of great concern. Currently, alternatives therapies are being sought to mitigate hepatic damage, and there has been growing interest in the research on bioactive phytochemical agents (nutraceuticals) due to their versatility in health benefits reported in various epidemiological studies. Therefore, this review provides information and summarizes the juncture of antiviral, immunomodulatory, and hepatoprotective nutraceuticals that can be useful during the management of COVID-19.

miR-205: A Potential Biomedicine for Cancer Therapy

microRNAs (miRNAs) are a class of small non-coding RNAs that regulate the expression of their target mRNAs post transcriptionally. miRNAs are known to regulate not just a gene but the whole gene network (signaling pathways). Accumulating evidence(s) suggests that miRNAs can work either as oncogenes or tumor suppressors, but some miRNAs have a dual nature since they can act as both. miRNA 205 (miR-205) is one such highly conserved miRNA that can act as both, oncomiRNA and tumor suppressor. However, most reports confirm its emerging role as a tumor suppressor in many cancers. This review focuses on the downregulated expression of miR-205 and discusses its dysregulation in breast, prostate, skin, liver, gliomas, pancreatic, colorectal and renal cancers. This review also confers its role in tumor initiation, progression, cell proliferation, epithelial to mesenchymal transition, and tumor metastasis. Restoration of miR-205 makes cells more sensitive to drug treatments and mitigates drug resistance. Additionally, the importance of miR-205 in chemosensitization and its utilization as potential biomedicine and nanotherapy is described. Together, this review research article sheds a light on its application as a diagnostic and therapeutic marker, and as a biomedicine in cancer.

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.