Viruses.STRING: A Virus-Host Protein-Protein Interaction Database

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

Unraveling the anti-breast cancer activity of Cimicifugae rhizoma using biological network pathways and molecular dynamics simulation

Cimicifugae is a commonly used treatment for breast cancer, but the specific molecular mechanisms underlying its effectiveness remain unclear. In this research, we employ a combination of network pharmacology, molecular docking, and molecular dynamics simulations to uncover the most potent phytochemical within Cimicifugae rhizoma in order to delve into its interaction with the target protein in breast cancer treatment. We identified 18 active compounds and 89 associated targets, primarily associated to various biological processes such as lipid metabolism, the signaling pathway in diabetes, viral infections, and cancer-related pathways. Molecular docking analysis revealed that the two most active compounds, Formononetin and Cimigenol, exhibit strong binding to the target protein AKT1. Through molecular dynamics simulations, we found that the Cimigenol-AKT1 complex exhibits greater structural stability and lower interaction energy compared to the stigmasterol-AKT1 complex. Our study demonstrates that Cimicifugae rhizoma exerts its effects in breast cancer treatment through a multi-component, multi-target synergistic approach. Furthermore, we propose that Cimigenol, targeting AKT-1, represents the most effective compound, offering valuable insights into the molecular mechanisms underpinning its role in breast cancer therapy.

Genotypic Profiling, Functional Analysis, Cholesterol-Lowering Ability, and Angiotensin I-Converting Enzyme (ACE) Inhibitory Activity of Probiotic Lactiplantibacillus plantarum K25 via Different Approaches

Due to its alleged health advantages, several uses in biotechnology and food safety, the well-known probiotic strain Lactiplantibacillus plantarum K25 has drawn interest. This in-depth investigation explores the genetic diversity, makeup, and security characteristics of the microbial genome of L. plantarum K25, providing insightful knowledge about its genotypic profile and functional characteristics. Utilizing cutting-edge bioinformatics techniques like comparative genomics, pan-genomics, and genotypic profiling was carried out to reveal the strain's multidimensional potential in various fields. The results not only add to our understanding of the genetic makeup of L. plantarum K25 but also show off its acceptability in various fields, notably in biotechnology and food safety. The explanation of evolutionary links, which highlights L. plantarum K25's aptitude as a probiotic, is one notable finding from this research. Its safety profile, which is emphasized by the absence of genes linked to antibiotic resistance, is crucial and supports its status as a promising probiotic option.

The activation of HAMP promotes colorectal cancer cell proliferation through zinc-mediated upregulation of SMAD4 expression

Background

Colorectal cancer (CRC) poses a significant challenge in digestive system diseases, and emerging evidence underscores the critical role of zinc metabolism in its progression. This study aimed to investigate the clinical implications of genes at the intersection of zinc metabolism and CRC.

Methods

We downloaded CRC prognosis-related genes and zinc metabolism-related genes from public databases. Then, the overlapping genes were screened out, and bioinformatics analysis was performed to obtain the hub gene associated with CRC prognosis. Subsequently, in vitro assays were carried out to investigate the expression of this hub gene and its exact mechanism between zinc metabolism and CRC.

Results

HAMP was identified as the hub CRC prognostic gene from overlapping zinc metabolism-related and CRC prognostic genes. In vitro analysis showed HAMP was over-expressed in CRC, and its knockdown inhibited RKO and HCT-116 cell invasion and migration significantly. ZnSO4 induced HAMP up-regulation to promote cell proliferation, while TPEN decreased HAMP expression to inhibit cell proliferation. Importantly, we further found that ZnSO4 enhanced SMAD4 expression to augment HAMP promoter activity and promote cell proliferation in CRC.

Conclusions

HAMP stands out as an independent prognostic factor in CRC, representing a potential therapeutic target. Its intricate regulation by zinc, particularly through the modulation of SMAD4, unveils a novel avenue for understanding CRC biology. This study provides valuable insights into the interplay between zinc metabolism, HAMP, and CRC, offering promising clinical indicators for CRC patients. The findings present a compelling case for further exploration and development of targeted therapeutic strategies in CRC management.

Analysis of the Potential Angiogenic Mechanisms of BuShenHuoXue Decoction against Osteonecrosis of the Femoral Head Based on Network Pharmacology and Experimental Validation

OBJECTIVE

Osteonecrosis of the femoral head (ONFH) is a common orthopedic disease with a high disability rate. The clinical effect of BuShenHuoXue decoction (BSHX) for ONFH is satisfactory. We aimed to elucidate the potential angiogenic mechanisms of BSHX in a rat femoral osteonecrosis model and bone marrow mesenchymal stem cells (BMSCs).

METHODS

With in vivo experiments, we established the steroid-induced osteonecrosis of the femoral head (SONFH) model using Sprague-Dawley (SD) rats (8-week-old). The rats were randomly divided into five group of 12 rats each and given the corresponding interventions: control, model (gavaged with 0.9% saline), BSHX low-, medium- and high-dose groups (0.132 3, 0.264 6, and 0.529 2 g/mL BSHX solution by gavage). After 12 weeks, haematoxylin and eosin (H&E) staining was preformed to evaluate rat osteonecrosis. the expression of angiogenic factors (CD31, VEGFA, KDR, VWF) in rat femoral head was detected by immunohistochemistry, qPCR and western blotting. In cell experiment, BMSCs were isolated and cultured in the femoral bone marrow cavity of 4-week-old SD rats. BMSCs were randomly divided into eight groups and intervened with different doses of BSHX-containing serum and glucocorticoids: control group (CG); BSHX low-, medium-, and high-dose groups (CG + 0.661 5, 1.323, and 2.646 g/kg BSHX gavage rat serum); dexamethasone (Dex) group; and Dex + BSHX low-, medium-, and high-dose groups (Dex + 0.661 5, 1.323, and 2.646 g/kg BSHX gavaged rat serum), the effects of BSHX-containing serum on the angiogenic capacity of BMSCs were examined by qPCR and Western blotting. A co-culture system of rat aortic endothelial cells (RAOECs) and BMSCs was then established. Migration and angiogenesis of RAOECs were observed using angiogenesis and transwell assay. Identification of potential targets of BSHX against ONFH was obtained using network pharmacology.

RESULTS

BSHX upregulated the expression of CD31, VEGFA, KDR, and VWF in rat femoral head samples and BMSCs (p < 0.05, vs. control group or model group). Different concentrations of BSHX-containing serum significantly ameliorated the inhibition of CD31, VEGFA, KDR and VWF expression by high concentrations of Dex. BSHX-containing serum-induced BMSCs promoted the migration and angiogenesis of RAOECs, reversed to some extent the adverse effect of Dex on microangiogenesis in RAOECs, and increased the number of microangiogenic vessels. Furthermore, we identified VEGFA, COL1A1, COL3A1, and SPP1 as important targets of BSHX against ONFH.

CONCLUSION

BSHX upregulated the expression of angiogenic factors in the femoral head tissue of ONFH model rats and promoted the angiogenic capacity of rat RAOECs and BMSCs. This study provides an important basis for the use of BSHX for ONFH prevention and treatment.

Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients

As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

Multi-omics data integration reveals the complexity and diversity of host factors associated with influenza virus infection

Influenza viruses pose a significant and ongoing threat to human health. Many host factors have been identified to be associated with influenza virus infection. However, there is currently a lack of an integrated resource for these host factors. This study integrated human genes and proteins associated with influenza virus infections for 14 subtypes of influenza A viruses, as well as influenza B and C viruses, and built a database named H2Flu to store and organize these genes or proteins. The database includes 28,639 differentially expressed genes (DEGs), 1,850 differentially expressed proteins, and 442 proteins with differential posttranslational modifications after influenza virus infection, as well as 3,040 human proteins that interact with influenza virus proteins and 57 human susceptibility genes. Further analysis showed that the dynamic response of human cells to virus infection, cell type and strain specificity contribute significantly to the diversity of DEGs. Additionally, large heterogeneity was also observed in protein-protein interactions between humans and different types or subtypes of influenza viruses. Overall, the study deepens our understanding of the diversity and complexity of interactions between influenza viruses and humans, and provides a valuable resource for further studies on such interactions.

A primer on correlation-based dimension reduction methods for multi-omics analysis

The continuing advances of omic technologies mean that it is now more tangible to measure the numerous features collectively reflecting the molecular properties of a sample. When multiple omic methods are used, statistical and computational approaches can exploit these large, connected profiles. Multi-omics is the integration of different omic data sources from the same biological sample. In this review, we focus on correlation-based dimension reduction approaches for single omic datasets, followed by methods for pairs of omics datasets, before detailing further techniques for three or more omic datasets. We also briefly detail network methods when three or more omic datasets are available and which complement correlation-oriented tools. To aid readers new to this area, these are all linked to relevant R packages that can implement these procedures. Finally, we discuss scenarios of experimental design and present road maps that simplify the selection of appropriate analysis methods. This review will help researchers navigate emerging methods for multi-omics and integrating diverse omic datasets appropriately. This raises the opportunity of implementing population multi-omics with large sample sizes as omics technologies and our understanding improve.

Functional Landscape of African Swine Fever Virus-Host and Virus-Virus Protein Interactions

Viral replication fully relies on the host cell machinery, and physical interactions between viral and host proteins mediate key steps of the viral life cycle. Therefore, identifying virus-host protein-protein interactions (PPIs) provides insights into the molecular mechanisms governing virus infection and is crucial for designing novel antiviral strategies. In the case of the African swine fever virus (ASFV), a large DNA virus that causes a deadly panzootic disease in pigs, the limited understanding of host and viral targets hinders the development of effective vaccines and treatments. This review summarizes the current knowledge of virus-host and virus-virus PPIs by collecting and analyzing studies of individual viral proteins. We have compiled a dataset of experimentally determined host and virus protein targets, the molecular mechanisms involved, and the biological functions of the identified virus-host and virus-virus protein interactions during infection. Ultimately, this work provides a comprehensive and systematic overview of ASFV interactome, identifies knowledge gaps, and proposes future research directions.

Comparative genomics and integrated system biology approach unveiled undirected phylogeny patterns, mutational hotspots, functional patterns, and molecule repurposing for monkeypox virus

Monkeypox is a viral zoonosis with symptoms that are reminiscent of those experienced in previous smallpox cases. The GSAID database (Global Initiative on Sharing Avian Influenza Data) was used to assess 630 genomes of MPXV. The phylogenetic study revealed six primary clades, as well as a smaller percentage in radiating clades. Individual clades that make up various nationalities may have formed as a result of a particular SNP hotspot type that mutated in a specific population. The most significant mutation based on a mutational hotspot analysis was found at G3729A and G5143A. The gene ORF138, which encodes the Ankyrin repeat (ANK) protein, was found to have the most mutations. This protein mediates molecular recognition via protein-protein interactions. It was shown that 243 host proteins interacted with 10 monkeypox proteins identified as the hub proteins E3, SPI2, C5, K7, E8, G6, N2, B14, CRMB, and A41 through 262 direct connections. The interaction with chemokine system-related proteins provides further evidence that the monkeypox virus suppresses human proteins to facilitate its survival against innate immunity. Several FDA-approved molecules were evaluated as possible inhibitors of F13, a significant envelope protein on the membrane of extracellular versions of the virus. A total of 2500 putative ligands were individually docked with the F13 protein. The interaction between the F13 protein and these molecules may help prevent the monkeypox virus from spreading. After being confirmed by experiments, these putative inhibitors could have an impact on the activity of these proteins and be used in monkeypox treatments.

RNA-Sequencing Characterization of lncRNA and mRNA Functions in Septic Pig Liver Injury

We assessed differentially expressed (DE) mRNAs and lncRNAs in the liver of septic pigs to explore the key factors regulating lipopolysaccharide (LPS)-induced liver injury. We identified 543 DE lncRNAs and 3642 DE mRNAs responsive to LPS. Functional enrichment analysis revealed the DE mRNAs were involved in liver metabolism and other pathways related to inflammation and apoptosis. We also found significantly upregulated endoplasmic reticulum stress (ERS)-associated genes, including the receptor protein kinase receptor-like endoplasmic reticulum kinase (PERK), the eukaryotic translation initiation factor 2α (EIF2S1), the transcription factor C/EBP homologous protein (CHOP), and activating transcription factor 4 (ATF4). In addition, we predicted 247 differentially expressed target genes (DETG) of DE lncRNAs. The analysis of protein-protein interactions (PPI) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway detected key DETGs that are involved in metabolic pathways, such as N-Acetylgalactosaminyltransferase 2 (GALNT2), argininosuccinate synthetase 1 (ASS1), and fructose 1,6-bisphosphatase 1 (FBP1). LNC_003307 was the most abundant DE lncRNA in the pig liver, with a marked upregulation of >10-fold after LPS stimulation. We identified three transcripts for this gene using the rapid amplification of the cDNA ends (RACE) technique and obtained the shortest transcript sequence. This gene likely derives from the nicotinamide N-methyltransferase (NNMT) gene in pigs. According to the identified DETGs of LNC_003307, we hypothesize that this gene regulates inflammation and endoplasmic reticulum stress in LPS-induced liver damage in pigs. This study provides a transcriptomic reference for further understanding of the regulatory mechanisms underlying septic hepatic injury.

Tongqiao Huoxue decoction alleviates neurological impairment following ischemic stroke via the PTGS2/NF-kappa B axis

Traditional Chinese medicine has emerged as promising targets for ischemic stroke (IS) therapy, yet the mechanism remains elusive. The current study was performed with an aim to investigate the action and mechanism of Tongqiao Huoxue decoction (TQHXD) affecting the neurological impairment secondary to IS based on network pharmacology. Based on network pharmacology and bioinformatics analysis, target genes and pathways involved in the treatment of TQHXD against IS were predicted. Serum containing TQHXD was prepared through blood collection from C57BL/6 mice after intragastric administration of TQHXD. The main results exhibited that Prostaglandin-endoperoxide synthase 2 (PTGS2) exhibited an abundance in IS and enrichment in the NF-kappa B signaling pathway, holding the potential as targets related to TQHXD treatment for IS. TQHXD was found to rescue cell viability, inhibit apoptosis, and alleviate inflammation under oxygen and glucose deprivation and reoxygenation (OGD/R) exposure. Furthermore, our in vivo experiment validated the protective function of TQHXD in ischemic brain damage stimulated by middle cerebral artery occlusion (MCAO). This protective action of TQHXD could be attenuated by overexpressing nuclear factor (NF)-kappa B, which was dependent on PTGS2. Collectively, TQHXD was demonstrated to ameliorate IS-induced neurological impairment by blocking the NF-kappa B signaling pathway and down-regulating PTGS2.

Potential Targets and Mechanisms of Jiedu Quyu Ziyin Decoction for Treating SLE-GIOP: Based on Network Pharmacology and Molecular Docking

Background

Systemic lupus erythematosus (SLE) is characterized by poor regulation of the immune response leading to chronic inflammation and multiple organ dysfunction. Glucocorticoid (GC) is currently one of the main treatments. However, a high dose or prolonged use of GC may result in glucocorticoid-induced osteoporosis (GIOP). Jiedu Quyu Ziyin decoction (JP) is effective in treating SLE and previous clinical studies have proved that JP can prevent and treat SLE steroid osteoporosis (SLE-GIOP). We aim to examine JPs main mechanism on SLE-GIOP through network pharmacology and molecular docking.

Methods

TCMSP and TCMID databases were used to screen potential active compounds and targets of JP. The SLE-GIOP targets are collected from GeneCards, OMIM, PharmGkb, TTD, and DrugBank databases. R software was used to obtain the cross-targets of JP and SLE-GIOP and to perform GO and KEGG enrichment analysis. Cytoscape software was used to make the Chinese Medicines-Active Ingredient-Intersection Targets network diagram. STRING database construct protein-protein interaction network and obtain the core targets. Auto Dock Tools and Pymol software were used for docking.

Results

Fifty eight targets overlapped between JP and SLE-GIOP were suggested as potential targets of JP in the treatment of SLE-GIOP. Network topology analysis identified five core targets. GO enrichment analysis was obtained 1,968 items, and the top 10 biological process, closeness centrality, and molecular function were displayed. A total of 154 signaling pathways were obtained by KEGG enrichment analysis, and the top 30 signaling pathways were displayed. JP was well bound by MAPK1, TP53, and MYC according to the molecular docking results.

Conclusion

We investigated the potential targets and signaling pathways of JP against SLE-GIOP in this study. It shows that JP is most likely to achieve the purpose of treating SLE-GIOP by promoting the proliferation and differentiation of osteoblasts. A solid theoretical foundation will be provided for the future study of clinical and experimental topics.

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.

An in silico and in vitro integrated analysis method to reveal the curative mechanisms and pharmacodynamic substances of Bufei granule on chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease with high disability and mortality. Clinical studies have shown that the Traditional Chinese Medicine Bufei Granule (BFG) has conspicuous effects on relieving cough and improving lung function in patients with COPD and has a reliable effect on the treatment of COPD, whereas the therapeutic mechanism is vague. In the present study, the latent bronchodilators and mechanism of BFG in the treatment of COPD were discussed through the method of network pharmacology. Then, the molecular docking and molecular dynamics simulation were performed to calculate the binding efficacy of corresponding compounds in BFG to muscarinic receptor. Finally, the effects of BFG on bronchial smooth muscle were validated by in vitro experiments. The network pharmacology results manifested the anti-COPD effect of BFG was mainly realized via restraining airway smooth muscle contraction, activating cAMP pathways and relieving oxidative stress. The results of molecular docking and molecular dynamics simulation showed alpinetin could bind to cholinergic receptor muscarinic 3. The in vitro experiment verified both BFG and alpinetin could inhibit the levels of CHRM3 and acetylcholine and could be potential bronchodilators for treating COPD. This study provides an integrating network pharmacology method for understanding the therapeutic mechanisms of traditional Chinese medicine, as well as a new strategy for developing natural medicines for treating COPD.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Identification of key pharmacological components and targets for Aidi injection in the treatment of pancreatic cancer by UPLC-MS, network pharmacology, and in vivo experiments

BACKGROUND

Pancreatic cancer is one of the most lethal cancers worldwide. Aidi injection (ADI) is a representative antitumor medication based on Chinese herbal injection, but its antitumor mechanisms are still poorly understood.

MATERIALS AND METHODS

In this work, the subcutaneous xenograft model of human pancreatic cancer cell line Panc-1 was established in nude mice to investigate the anticancer effect of ADI in vivo. We then determined the components of ADI using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and explored the possible molecular mechanisms against pancreatic cancer using network pharmacology.

RESULTS

In vivo experiments, the volume, weight, and degree of histological abnormalities of implanted tumors were significantly lower in the medium and high concentration ADI injection groups than in the control group. Network pharmacology analysis identified four active components of ADI and seven key targets, TNF, VEGFA, HSP90AA1, MAPK14, CASP3, P53 and JUN. Molecular docking also revealed high affinity between the active components and the target proteins, including Astragaloside IV to P53 and VEGFA, Ginsenoside Rb1 to CASP3 and Formononetin to JUN.

CONCLUSION

ADI could reduce the growth rate of tumor tissue and alleviate the structural abnormalities in tumor tissue. ADI is predicted to act on VEGFA, P53, CASP3, and JUN in ADI-mediated treatment of pancreatic cancer.

PRO-Simat: Protein network simulation and design tool

PRO-Simat is a simulation tool for analysing protein interaction networks, their dynamic change and pathway engineering. It provides GO enrichment, KEGG pathway analyses, and network visualisation from an integrated database of more than 8 million protein-protein interactions across 32 model organisms and the human proteome. We integrated dynamical network simulation using the Jimena framework, which quickly and efficiently simulates Boolean genetic regulatory networks. It enables simulation outputs with in-depth analysis of the type, strength, duration and pathway of the protein interactions on the website. Furthermore, the user can efficiently edit and analyse the effect of network modifications and engineering experiments. In case studies, applications of PRO-Simat are demonstrated: (i) understanding mutually exclusive differentiation pathways in Bacillus subtilis, (ii) making Vaccinia virus oncolytic by switching on its viral replication mainly in cancer cells and triggering cancer cell apoptosis and (iii) optogenetic control of nucleotide processing protein networks to operate DNA storage. Multilevel communication between components is critical for efficient network switching, as demonstrated by a general census on prokaryotic and eukaryotic networks and comparing design with synthetic networks using PRO-Simat. The tool is available at https://prosimat.heinzelab.de/ as a web-based query server.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Virulence network of interacting domains of influenza a and mouse proteins

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD₅₀ values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

Cytoscape stringApp 2.0: Analysis and Visualization of Heterogeneous Biological Networks

Biological networks are often used to represent complex biological systems, which can contain several types of entities. Analysis and visualization of such networks is supported by the Cytoscape software tool and its many apps. While earlier versions of stringApp focused on providing intraspecies protein-protein interactions from the STRING database, the new stringApp 2.0 greatly improves the support for heterogeneous networks. Here, we highlight new functionality that makes it possible to create networks that contain proteins and interactions from STRING as well as other biological entities and associations from other sources. We exemplify this by complementing a published SARS-CoV-2 interactome with interactions from STRING. We have also extended stringApp with new data and query functionality for protein-protein interactions between eukaryotic parasites and their hosts. We show how this can be used to retrieve and visualize a cross-species network for a malaria parasite, its host, and its vector. Finally, the latest stringApp version has an improved user interface, allows retrieval of both functional associations and physical interactions, and supports group-wise enrichment analysis of different parts of a network to aid biological interpretation. stringApp is freely available at https://apps.cytoscape.org/apps/stringapp.

An in silico comparative transcriptome analysis identifying hub lncRNAs and mRNAs in brain metastatic small cell lung cancer (SCLC)

Small cell lung cancer (SCLC) is a particularly lethal subtype of lung cancer. Metastatic lung tumours lead to most deaths from lung cancer. Predicting and preventing tumour metastasis is crucially essential for patient survivability. Hence, in the current study, we focused on a comprehensive analysis of lung cancer patients' differentially expressed genes (DEGs) on brain metastasis cell lines. DEGs are analysed through KEGG and GO databases for the most critical biological processes and pathways for enriched DEGs. Additionally, we performed protein-protein interaction (PPI), GeneMANIA, and Kaplan-Meier survival analyses on our DEGs. This article focused on mRNA and lncRNA DEGs for LC patients with brain metastasis and underlying molecular mechanisms. The expression data was gathered from the Gene Expression Omnibus database (GSE161968). We demonstrate that 30 distinct genes are up-expressed in brain metastatic SCLC patients, and 31 genes are down-expressed. All our analyses show that these genes are involved in metastatic SCLC. PPI analysis revealed two hub genes (CAT and APP). The results of this article present three lncRNAs, Including XLOC_l2_000941, LOC100507481, and XLOC_l2_007062, also notable mRNAs, have a close relation with brain metastasis in lung cancer and may have a role in the epithelial-mesenchymal transition (EMT) in tumour cells.

Rational drug repositioning for coronavirus-associated diseases using directional mapping and side-effect inference

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen of coronavirus disease 2019 (COVID-19), has infected hundreds of millions of people and caused millions of deaths. Looking for valid druggable targets with minimal side effects for the treatment of COVID-19 remains critical. After discovering host genes from multiscale omics data, we developed an end-to-end network method to investigate drug-host gene(s)-coronavirus (CoV) paths and the mechanism of action between the drug and the host factor in a directional network. We also inspected the potential side effect of the candidate drug on several common comorbidities. We established a catalog of host genes associated with three CoVs. Rule-based prioritization yielded 29 Food and Drug Administration (FDA)-approved drugs via accounting for the effects of drugs on CoVs, comorbidities, and drug-target confidence information. Seven drugs are currently undergoing clinical trials as COVID-19 treatment. This catalog of druggable host genes associated with CoVs and the prioritized repurposed drugs will provide a new sight in therapeutics discovery for severe COVID-19 patients.

Network Pharmacology and Molecular Docking of Shiwei Qingwen Decoction Reveal TNF as a Potential Target for Alleviating Mild COVID-19 Symptoms

Objective: Shiwei Qingwen decoction (SWQWD) is effective in preventing COVID-19. This study examined the active components of SWQWD and its potential targets for preventing COVID-19. The study used network pharmacology and molecular docking technology to verify the role of SWQWD targets through animal experiments and explored the mechanisms that enhance immunity to alleviate mild COVID-19 symptoms. Methods: First, SWQWD- and COVID-19-related targets were retrieved from TCMSP, GeneCards, and OMIM databases. Second, protein–protein interaction networks were established using the String database. The drug active ingredient target network was constructed in Cytoscape to identify the core target proteins. Third, Gene Ontology (GO) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the SWQWD mechanism of action. Finally, the targets were validated by molecular docking in an acute lung injury (ALI) rat model. Results: The SWQWD compound target network contained 79 compounds and 277 targets, coinciding with the 73 targets of COVID-19. The most important gene in the core subnetwork was a tumor necrosis factor (TNF). The 3 most potent compounds, quercetin, kaempferol, and luteolin, can enter the active pockets of TNF and have potential therapeutic roles in COVID-19. Conclusion: Quercetin, kaempferol, and luteolin in SWQWD may enhance immunity by regulating multiple TNF signal pathways. After administering SWQWD, the content of tumor necrosis factor-α was significantly reduced in the bronchoalveolar lavage fluid (BALF) of ALI rats in comparison to the model group. We believe SWQWD is able to prevent and control COVID-19 through the target of TNF.

Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways

Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions.

Identification of immune and stromal cell infiltration-related gene signature for prognosis prediction in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is a common and life-threatening hematologic malignancy, its occurrence and progression are closely related to immune/stromal cell infiltration in the bone marrow (BM) microenvironment. However, no studies have described an immune/stromal cell infiltration-related gene (ISCIRG)-based prognostic signature for ALL. A total of 444 patients involving 437 bulk and 7 single-cell RNA-seq datasets were included in this study. Eligible datasets were searched and reviewed from the database of TCGA, TARGET project and GEO. Then an integrated bioinformatics analysis was performed to select optimal prognosis-related genes from ISCIRGs, construct a nomogram model for predicting prognosis, and assess the predictive power. After LASSO and multivariate Cox regression analyses, a seven ISCIRGs-based signature was proved to be able to significantly stratify patients into high- and low-risk groups in terms of OS. The seven genes were confirmed that directly related to the composition and status of immune/stromal cells in BM microenvironment by analyzing bulk and single-cell RNA-seq datasets. The calibration plot showed that the predicted results of the nomogram were consistent with the actual observation results of training/validation cohort. This study offers a reference for future research regarding the role of ISCIRGs in ALL and the clinical care of patients.

Unraveling the multi-targeted curative potential of bioactive molecules against cervical cancer through integrated omics and systems pharmacology approach

Molecular level understanding on the role of viral infections causing cervical cancer is highly essential for therapeutic development. In these instances, systems pharmacology along with multi omics approach helps in unraveling the multi-targeted mechanisms of novel biologically active compounds to combat cervical cancer. The immuno-transcriptomic dataset of healthy and infected cervical cancer patients was retrieved from the array express. Further, the phytocompounds from medicinal plants were collected from the literature. Network Analyst 3.0 has been used to identify the immune genes around 384 which are differentially expressed and responsible for cervical cancer. Among the 87 compounds reported in plants for treating cervical cancer, only 79 compounds were targeting the identified immune genes of cervical cancer. The significant genes responsible for the domination in cervical cancer are identified in this study. The virogenomic signatures observed from cervical cancer caused by E7 oncoproteins serve as the potential therapeutic targets whereas, the identified compounds can act as anti-HPV drug deliveries. In future, the exploratory rationale of the acquired results will be useful in optimizing small molecules which can be a viable drug candidate.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

The Landscape of Virus-Host Protein–Protein Interaction Databases

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

Virus-host interaction network analysis in Colorectal cancer identifies core virus network signature and small molecules

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Systematic analysis of virus-host networks identified key pathways in CRC.

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Core virus-CRC network revealed the growth pathway regulated by viruses.

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Short linear motif analysis identified druggable regions in virus proteins.

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Virtual screening revealed key anti-viral molecules against viral proteins.

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Molecular dynamics simulations showed the effect of anti-viral molecules.

Colorectal cancer (CRC) is a significant contributor to cancer-related deaths caused by an unhealthy lifestyle. Multiple studies reveal that viruses are involved in colorectal tumorigenesis. The viruses such as Human Cytomegalovirus (HCMV), Human papillomaviruses (HPV16 & HPV18), and John Cunningham virus (JCV) are known to cause colorectal cancer. The molecular mechanisms of cancer genesis and maintenance shared by these viruses remain unclear. We analysed the virus-host networks and connected them with colorectal cancer proteome datasets and extracted the core shared interactions in the virus-host CRC network. Our network topology analysis identified prominent virus proteins RL6 (HCMV), VE6 (HPV16 and HPV18), and Large T antigen (JCV). Sequence analysis uncovered short linear motifs (SLiMs) in each viral target. We used these targets to identify the antiviral drugs through a structure-based virtual screening approach. This analysis highlighted that temsavir, pimodivir, famotine, and bictegravir bind to each virus protein target, respectively. We also assessed the effect of drug binding using molecular dynamic simulations, which shed light on the modulatory effect of drug molecules on SLiM regions in viral targets. Hence, our systematic screening of virus-host networks revealed viral targets, which could be crucial for cancer therapy.

PHILM2Web: A high-throughput database of macromolecular host–pathogen interactions on the Web

During infection, the pathogen’s entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host–pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein–protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen–Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing.

Database URL: http://philm2web.live

Viral hijacking mechanism in humans through protein-protein interactions

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

Non-Cytokine Protein Profile of the Mesenchymal Stem Cell Secretome That Regulates the Androgen Production Pathway

Polycystic ovary syndrome (PCOS) is the most common endocrine and metabolic disorder in reproductive-aged women, and it typically involves elevated androgen levels. Recently, it has been reported that human bone marrow mesenchymal stem cells (hBM-MSCs) can regulate androgen synthesis pathways. However, the details of the mechanism are still unclear. hBM-MSC-derived secreted factors (the secretome) are promising sources of cell-based therapy as they consist of various types of proteins. It is thus important to know which proteins interact with disease-implicated biomolecules. This work aimed to investigate which secretome components contain the key factor that inhibits testosterone synthesis. In this study, we fractionated hBM-MSC-conditioned media into three fractions based on their molecular weights and found that, of the three fractions, one had the ability to inhibit the androgen-producing genes efficiently. We also analyzed the components of this fraction and established a protein profile of the hBM-MSC secretome, which was shown to inhibit androgen synthesis. Our study describes a set of protein components present in the hBM-MSC secretome that can be used therapeutically to treat PCOS by regulating androgen production for the first time.

Exploration of the Immuno-Inflammatory Potential Targets of Xinfeng Capsule in Patients with Ankylosing Spondylitis Based on Data Mining, Network Pharmacology, and Molecular Docking

Objective

This study aimed to ascertain the immuno-inflammatory molecular targets of Xinfeng capsules (XFC) in the treatment of ankylosing spondylitis (AS) based on data mining, network pharmacology, and molecular docking.

Methods

The efficacy of XFC in the treatment of AS was assessed by clinical data mining. Network pharmacology was utilized to establish a network of the targets for XFC active ingredients in the treatment of AS. The binding mode and affinity of XFC active ingredients to the key targets for AS were predicted using molecular docking.

Results

XFC significantly diminished immuno-inflammatory indicators of AS. In total, 208 targets of XFC were obtained from the TCMSP database and 629 disease targets of AS were screened from the GeneCards database, which were intersected to yield 57 targets of XFC in the treatment of AS. Protein-protein interaction, gene ontology, and Kyoto genome encyclopedia analyses showed that XFC might activate TNF and NF-κB signaling pathways. Quercetin, kaempferol, triptolide, and formononetin had free binding energies < -9 kcal/mol to inflammatory targets (TNF and PTGS2) in the molecular docking analysis of XFC-active ingredients, indicating that TNF and PTGS2 might be the targets of the action of XFC.

Conclusions

Collectively, XFC had a significant therapeutic effect on AS. Specifically, the active ingredients of XFC, including quercetin, kaempferol, triptolide, and formononetin, inhibited the inflammatory response in AS by downregulating TNF and PTGS2 in the TNF and NF-κB signaling pathways.

Over-Expression and Prognostic Significance of FN1, Correlating With Immune Infiltrates in Thyroid Cancer

Background

Thyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers.

Methods

Three Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein-protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1.

Results

FN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4+ T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and the expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical.

Conclusion

Our findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as an immunity-related biomarker and therapeutic target in THCA.

Investigating the Molecular Mechanism of Xijiao Dihuang Decoction for the Treatment of SLE Based on Network Pharmacology and Molecular Docking Analysis

Objective

To elucidate the main mechanism of Xijiao Dihuang decoction (XJDHT) for the treatment of systemic lupus erythematosus (SLE).

Methods

TCMSP, BATMAN-TCM, ETCM, and TCMID databases and literature search were used to screen the potential active compounds of XJDHT, and TCMSP and SwissProt databases were searched to predict the targets of the compounds. The targets of SLE were obtained from Genegards, OMIM, and DisGeNET databases, and Venn online platform was used to obtain the intersection targets of XJDHT and SLE. Afterwards, the PPI network was constructed by using the STRING database, and the core targets were identified by network topology analysis. GO and KEGG enrichment analyses were performed through R software, and molecular docking of the top three core targets and their corresponding compounds were accomplished by Autodock Vina and Pymol softwares.

Results

There were 30 potential active ingredients, 289 potential targets, and 129 intersection targets screened from the above databases. Network topology analysis identified 23 core targets, such as AKT1, TNF, IL6, IL1B, and INS. GO enrichment analysis obtained 2555 terms and mainly clustering on the react to lipopolysaccharide, membrane raft, and ubiquitin-like protein ligase binding. KEGG enrichment analysis obtained 187 signaling pathways, mainly concentrating on the lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress, and atherosclerosis. Molecular docking verified that the active compounds of XJDHT have the strong binding activity to the core targets.

Conclusion

This study preliminarily uncovers the mechanism of XJDHT acting on SLE through a “multicompound, multitarget, and multipathway” manner. XJDHT may achieve the treatment of SLE by inhibiting the proinflammatory factors, inflammatory signal cvtokines, proliferation, injury, and apoptosis processes. In summary, the present study would provide a promising theoretical basis for further clinical and experimental studies.

Analysis of protein association networks regulating the neuroactive metabolites production in Lactobacillus species

Human population is intensively suffering from mental disorders and stress. Microbial metabolites may alter the brain activity, which seems to be an effective approach in the treatment of psychological distress. Earlier, microbial neuroactive metabolites such as trimethylamine, imidazolone propionate and taurine have been shown to alter the brain activity. In the present study proteins regulating their production and activity were explored in Lactobacillus species with the help of STRING (11.5) as a bioinformatic tool. Dataset network of urocanate hydratase, glycine radical enzyme and taurine ABC transporter protein (ATP-dependent transporter) have been identified in Lactobacillus nodensis, Lactobacillus vini and Lactobacillus paraplantarum strains. Further, cluster analysis of network resulted with groups of homologous proteins that most likely related to reductive monocarboxylic acid cycle, pyruvate fermentation to acetate IV and L-histidine degradation I pathway. The findings emphasize on the use and evaluation of selected Lactobacillus strains as psychoactive bacteria for the prevention and treatment of certain neurological and neurophysiological conditions.

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Human-Soybean Allergies: Elucidation of the Seed Proteome and Comprehensive Protein-Protein Interaction Prediction

The soybean crop, Glycine max (L.) Merr., is consumed by humans, Homo sapiens, worldwide. While the respective bodies of literature and -omics data for each of these organisms are extensive, comparatively few studies investigate the molecular biological processes occurring between the two. We are interested in elucidating the network of protein-protein interactions (PPIs) involved in human-soybean allergies. To this end, we leverage state-of-the-art sequence-based PPI predictors amenable to predicting the enormous comprehensive interactome between human and soybean. A network-based analytical approach is proposed, leveraging similar interaction profiles to identify candidate allergens and proteins involved in the allergy response. Interestingly, the predicted interactome can be explored from two complementary perspectives: which soybean proteins are predicted to interact with specific human proteins and which human proteins are predicted to interact with specific soybean proteins. A total of eight proteins (six specific to the human proteome and two to the soy proteome) have been identified and supported by the literature to be involved in human health, specifically related to immunological and neurological pathways. This study, beyond generating the most comprehensive human-soybean interactome to date, elucidated a soybean seed interactome and identified several proteins putatively consequential to human health.

Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches

During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.

MVIP: multi-omics portal of viral infection

Virus infections are huge threats to living organisms and cause many diseases, such as COVID-19 caused by SARS-CoV-2, which has led to millions of deaths. To develop effective strategies to control viral infection, we need to understand its molecular events in host cells. Virus related functional genomic datasets are growing rapidly, however, an integrative platform for systematically investigating host responses to viruses is missing. Here, we developed a user-friendly multi-omics portal of viral infection named as MVIP (https://mvip.whu.edu.cn/). We manually collected available high-throughput sequencing data under viral infection, and unified their detailed metadata including virus, host species, infection time, assay, and target, etc. We processed multi-layered omics data of more than 4900 viral infected samples from 77 viruses and 33 host species with standard pipelines, including RNA-seq, ChIP-seq, and CLIP-seq, etc. In addition, we integrated these genome-wide signals into customized genome browsers, and developed multiple dynamic charts to exhibit the information, such as time-course dynamic and differential gene expression profiles, alternative splicing changes and enriched GO/KEGG terms. Furthermore, we implemented several tools for efficiently mining the virus-host interactions by virus, host and genes. MVIP would help users to retrieve large-scale functional information and promote the understanding of virus-host interactions.

Interactome of human and SARS-CoV-2 proteins to identify human hub proteins associated with comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences

Background

Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.

Results

This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.

Conclusion

Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07657-4.

Key genes affecting the progression of nasopharyngeal carcinoma identified by RNA-sequencing and bioinformatic analysis

Background: The present work was conducted to screen the potential biomarkers affecting nasopharyngeal carcinoma (NPC) progression through RNA-sequencing (RNA-seq), bioinformatic analysis and functional experiments.

Materials and Methods: Six normal samples and five NPC clinical samples were collected for RNA-seq analysis. The expression levels in both groups were determined through student’s t-test. We identified genes of P < 0.01 as the differentially expressed genes (DEGs). In addition, gene set enrichment analysis (GSEA) was conducted. Afterwards, STRING V10 database was employed to extract protein interactions among the DEGs. Later, we established a protein-protein interaction (PPI) network, and used the Cytoscape software for network visualization. qRT-PCR was conducted to verify hub genes from clinical samples. Then, the function of CXCL10 in cell proliferation, apoptosis, invasion and migration was evaluated.

Results: A total of 2024 DEGs were identified, among which, 1449 were down-regulated and 575 were up-regulated. The PPI was constructed, and the hub genes including Insulin Like Growth Factor 1 (IGF1), C-X-C Motif Chemokine Ligand 10 (CXCL10), Interleukin 13 (IL13), Intercellular Adhesion Molecule 1 (ICAM1), G Protein Subunit Gamma Transducin 1 (GNGT1), Matrix Metallopeptidase 1 (MMP1), Neurexin 1 (NRXN1) and Matrix Metallopeptidase 3 (MMP3) were obtained. The expression levels of CXCL10, IGF1, MMP3, MMP1, ICAM1, and IL-13 were significantly up-regulated in tumor tissues. High expression levels of CXCL10, MMP3 and ICAM1 predicted poor prognosis of NPC patients. CXCL10 silencing suppressed NPC cell proliferation and migration.

Conclusions: CXCL10 may serve as a potential key gene affecting NPC genesis and progression.

Finding functional associations between prokaryotic virus orthologous groups: a proof of concept

Background

The field of viromics has greatly benefited from recent developments in metagenomics, with significant efforts focusing on viral discovery. However, functional annotation of the increasing number of viral genomes is lagging behind. This is highlighted by the degree of annotation of the protein clusters in the prokaryotic Virus Orthologous Groups (pVOGs) database, with 83% of its current 9518 pVOGs having an unknown function.

Results

In this study we describe a machine learning approach to explore potential functional associations between pVOGs. We measure seven genomic features and use them as input to a Random Forest classifier to predict protein–protein interactions between pairs of pVOGs. After systematic evaluation of the model’s performance on 10 different datasets, we obtained a predictor with a mean accuracy of 0.77 and Area Under Receiving Operation Characteristic (AUROC) score of 0.83. Its application to a set of 2,133,027 pVOG-pVOG interactions allowed us to predict 267,265 putative interactions with a reported probability greater than 0.65. At an expected false discovery rate of 0.27, we placed 95.6% of the previously unannotated pVOGs in a functional context, by predicting their interaction with a pVOG that is functionally annotated.

Conclusions

We believe that this proof-of-concept methodology, wrapped in a reproducible and automated workflow, can represent a significant step towards obtaining a more complete picture of bacteriophage biology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04343-w.

Integrative resource for network-based investigation of COVID-19 combinatorial drug repositioning and mechanism of action

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the primary drug targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary, pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is a first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review

Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.

Prediction of Human-Plasmodium vivax Protein Associations From Heterogeneous Network Structures Based on Machine-Learning Approach

Malaria caused by Plasmodium vivax can lead to severe morbidity and death. In addition, resistance has been reported to existing drugs in treating this malaria. Therefore, the identification of new human proteins associated with malaria is urgently needed for the development of additional drugs. In this study, we established an analysis framework to predict human-P. vivax protein associations using network topological profiles from a heterogeneous network structure of human and P. vivax, machine-learning techniques and statistical analysis. Novel associations were predicted and ranked to determine the importance of human proteins associated with malaria. With the best-ranking score, 411 human proteins were identified as promising proteins. Their regulations and functions were statistically analyzed, which led to the identification of proteins involved in the regulation of membrane and vesicle formation, and proteasome complexes as potential targets for the treatment of P. vivax malaria. In conclusion, by integrating related data, our analysis was efficient in identifying potential targets providing an insight into human-parasite protein associations. Furthermore, generalizing this model could allow researchers to gain further insights into other diseases and enhance the field of biomedical science.