Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics

Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data

With the availability of COVID-19-related clinical data, healthcare researchers can now explore the potential of computational technologies such as artificial intelligence (AI) and machine learning (ML) to discover biomarkers for accurate detection, early diagnosis, and prognosis for the management of COVID-19. However, the identification of biomarkers associated with survival and deaths remains a major challenge for early prognosis. In the present study, we have evaluated and developed AI-based prediction algorithms for predicting a COVID-19 patient's survival or death based on a publicly available dataset consisting of clinical parameters and protein profile data of hospital-admitted COVID-19 patients. The best classification model based on clinical parameters achieved a maximum accuracy of 89.47% for predicting survival or death of COVID-19 patients, with a sensitivity and specificity of 85.71 and 92.45%, respectively. The classification model based on normalized protein expression values of 45 proteins achieved a maximum accuracy of 89.01% for predicting the survival or death, with a sensitivity and specificity of 92.68 and 86%, respectively. Interestingly, we identified 9 clinical and 45 protein-based putative biomarkers associated with the survival/death of COVID-19 patients. Based on our findings, few clinical features and proteins correlate significantly with the literature and reaffirm their role in the COVID-19 disease progression at the molecular level. The machine learning-based models developed in the present study have the potential to predict the survival chances of COVID-19 positive patients in the early stages of the disease or at the time of hospitalization. However, this has to be verified on a larger cohort of patients before it can be put to actual clinical practice. We have also developed a webserver CovidPrognosis, where clinical information can be uploaded to predict the survival chances of a COVID-19 patient. The webserver is available at http://14.139.62.220/covidprognosis/.

Integration of genetically regulated gene expression and pharmacological library provides therapeutic drug candidates

Approaches toward new therapeutics using disease genomics, such as genome-wide association study (GWAS), are anticipated. Here, we developed Trans-Phar [integration of transcriptome-wide association study (TWAS) and pharmacological database], achieving in silico screening of compounds from a large-scale pharmacological database (L1000 Connectivity Map), which have inverse expression profiles compared with tissue-specific genetically regulated gene expression. Firstly we confirmed the statistical robustness by the application of the null GWAS data and enrichment in the true-positive drug–disease relationships by the application of UK-Biobank GWAS summary statistics in broad disease categories, then we applied the GWAS summary statistics of large-scale European meta-analysis (17 traits; n_average = 201 849) and the hospitalized COVID-19 (n = 900 687), which has urgent need for drug development. We detected potential therapeutic compounds as well as anisomycin in schizophrenia (false discovery rate (FDR)-q = 0.056) and verapamil in hospitalized COVID-19 (FDR-q = 0.068) as top-associated compounds. This approach could be effective in disease genomics-driven drug development.

Modeling regulatory network topology improves genome-wide analyses of complex human traits

Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. Here we develop a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. Our method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and previously undescribed trait-associated genes revealing biological and therapeutic insights.

Gallincin ameliorates colitis-associated inflammation and barrier function in mice based on network pharmacology prediction

Objective

To explore potential mechanisms and effects of gallincin on a mouse model of colitis induced by dextran sulfate sodium (DSS).

Methods

Network pharmacology analysis was used to predict the molecular mechanism of action of gallincin for treatment of colitis. Gallincin was administered orally to mice with DSS-induced colitis. Expression of tumor necrosis factor α (TNF-α), D-lactate, and interleukin-1β (IL-1β) and myeloperoxidase activity were assessed with real-time quantitative PCR and an enzyme-linked immunoassay, respectively. Expression of occludin, zonula occludens 1 (ZO-1), and phosphorylated extracellular signal-regulated protein kinase1/2 (p-ERK1/2) was analyzed with immunohistochemical staining and/or western blot assays.

Results

Using a network pharmacology approach, 12 mapping targets between gallincin and colitis were obtained, including ERK/mitogen-activated protein kinase. Further investigations in an experimental colitis mouse model showed that gallincin significantly ameliorated experimental colitis, reduced D-lactate levels, and remarkably increased occludin and ZO-1 expression, possibly in part by decreasing IL-1β, TNF-α, and p-ERK1/2 levels and inhibiting leukocyte penetration.

Conclusions

Gallincin regulated colonic barrier function and reduced colitis-associated inflammation, suggesting it is a promising drug for the treatment of ulcerative colitis.

Investigation of the Multi-Target Mechanism of Guanxin-Shutong Capsule in Cerebrovascular Diseases: A Systems Pharmacology and Experimental Assessment

Guanxin-Shutong capsule (GXSTC), a combination of Mongolian medicines and traditional herbs, has been clinically proven to be effective in treating cerebrovascular diseases (CBVDs). However, the underlying pharmacological mechanisms of GXSTC in CBVDs remain largely unknown. In this study, a combination of systems pharmacology and experimental assessment approach was used to investigate the bioactive components, core targets, and possible mechanisms of GXSTC in the treatment of CBVDs. A total of 15 main components within GXSTC were identified using high-performance liquid chromatography coupled with diode array detector (HPLC-DAD) and a literature research. Fifty-five common genes were obtained by matching 252 potential genes of GXSTC with 462 CBVD-related genes. Seven core components in GXSTC and 12 core genes of GXSTC on CBVDs were further determined using the protein-protein interaction (PPI) and component-target-pathway (C-T-P) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results predicted that the molecular mechanisms of GXSTC on CBVDs were mainly associated with the regulation of the vascular endothelial function, inflammatory response, and neuronal apoptosis. Molecular docking results suggested that almost all of core component-targets have an excellent binding activity (affinity < −5 kcal/mol). More importantly, in middle cerebral artery occlusion (MCAO) -injured rats, GXSTC significantly improved the neurological function, reduced the infarct volume, and decreased the percentage of impaired neurons in a dose-dependent manner. Western blotting results indicated that GXSTC markedly upregulated the expression of vascular endothelial growth factor A (VEGFA) and endothelial nitric oxide synthase (eNOS), while downregulating the expression of cyclooxygenase-2 (COX-2) and transcription factor AP-1 (c-Jun) in MCAO-injured rats. These findings confirmed our prediction that GXSTC exerts a multi-target synergetic mechanism in CBVDs by maintaining vascular endothelial function, inhibiting neuronal apoptosis and inflammatory processes. The results of this study may provide a theoretical basis for GXSTC research and the clinical application of GXSTC in CBVDs.

Systems Pharmacology and In Silico Docking Analysis Uncover Association of CA2, PPARG, RXRA, and VDR with the Mechanisms Underlying the Shi Zhen Tea Formula Effect on Eczema

Eczema is a complex chronic inflammatory skin disease impacted by environmental factors, infections, immune disorders, and deficiencies in skin barrier function. Shi Zhen Tea (SZT), derived from traditional Chinese medicine Xiao-Feng-San, has shown to be an effective integrative therapy for treating skin lesions, itching, and sleeping loss, and it facilitates reduction of topical steroid and antihistamine use in pediatric and adult patients with severe eczema. Yet, its active compounds and therapeutic mechanisms have not been elucidated. In this study, we sought to investigate the active compounds and molecular mechanisms of SZT in treating eczema using systems pharmacology and in silico docking analysis. SZT is composed of 4 medicinal herbs, Baizhu (Atractylodis macrocephalae rhizome), Jingjie (Schizonepetae herba), Kushen (Sophorae flavescentis radix), and Niubangzi (Arctii fructus). We first identified 51 active compounds from SZT and their 81 potential molecular targets by high-throughput computational analysis, from which we identified 4 major pathways including Th17 cell differentiation, metabolic pathways, pathways in cancer, and the PI3K-Akt signaling pathway. Through network analysis of the compound-target pathway, we identified hub molecular targets within these pathways including carbonic anhydrase II (CA2), peroxisome proliferator activated receptor γ (PPAR γ), retinoid X receptor α (RXRA), and vitamin D receptor (VDR). We further identified top 5 compounds including cynarine, stigmasterin, kushenol, β-sitosterol, and (24S)-24-propylcholesta-5-ene-3β-ol as putative key active compounds on the basis of their molecular docking scores with identified hub target proteins. Our study provides an insight into the therapeutic mechanism underlying multiscale benefits of SZT for eczema and paves the way for developing new and potentially more effective eczema therapies.

A Biochemical and Structural Understanding of TOM Complex Interactions and Implications for Human Health and Disease

The central role mitochondria play in cellular homeostasis has made its study critical to our understanding of various aspects of human health and disease. Mitochondria rely on the translocase of the outer membrane (TOM) complex for the bulk of mitochondrial protein import. In addition to its role as the major entry point for mitochondrial proteins, the TOM complex serves as an entry pathway for viral proteins. TOM complex subunits also participate in a host of interactions that have been studied extensively for their function in neurodegenerative diseases, cardiovascular diseases, innate immunity, cancer, metabolism, mitophagy and autophagy. Recent advances in our structural understanding of the TOM complex and the protein import machinery of the outer mitochondrial membrane have made structure-based therapeutics targeting outer mitochondrial membrane proteins during mitochondrial dysfunction an exciting prospect. Here, we describe advances in understanding the TOM complex, the interactome of the TOM complex subunits, the implications for the development of therapeutics, and our understanding of the structure/function relationship between components of the TOM complex and mitochondrial homeostasis.

Survival outcomes for patients with four treatments in stages I-III esophageal squamous cell carcinoma: a SEER analysis

BACKGROUND

Esophageal cancer (EC) is globally acknowledged as one of the most common malignancies among all gastrointestinal cancers. Furthermore, in Eastern Asia, squamous cell carcinoma is the main pathological type of EC. There are different treatments for esophageal squamous cell carcinoma (ESCC), but there is still a lack of large-sample analysis of prognosis among different treatments, especially for different tumor stages. The analysis of the prognosis of ESCC patients with different treatments may be helpful to choose the treatment methods for different stages ESCC.

METHODS

A total of 3,346 patients with pathological ESCC between 1976 and 2016 were derived from the Surveillance, Epidemiology, and End Results (SEER) database. All clinical factors associated with prognosis were collected and analyzed to achieve the difference of prognosis among different treatments in ESCC patients, such as ages, sex, race, tumor grade, anatomic location and so on. Kaplan-Meier and Cox proportional hazard analysis were used to compare survival of different treatments in ESCC patients with stage I-III.

RESULTS

The overall survival (OS) in all ESCC patients who had received surgery and surgery plus radiation therapy or/and chemotherapy are superior than that had not received any treatments and radiation therapy or/and chemotherapy. The OS in ESCC patients with stage I who had received surgery and surgery plus radiation therapy or/and chemotherapy are superior than that had not received any treatments and radiation therapy or/and chemotherapy. The OS in ESCC patients with stage II/III who had received surgery and surgery plus radiation therapy or/and chemotherapy are superior than that in other groups. Age, race and grade as an independent predictive factor for survival (P<0.05). A nomogram model was constructed to show surgery group had better 1-, 3- and 5-year OS than radiation therapy or/and chemotherapy group (OS: 78.5% vs. 59.2%, 37.9% vs. 18.4%, 16.9% vs. 6.1%).

CONCLUSIONS

Surgery is still the first choice for all ESCC patients with stage I-III. Radiotherapy and chemotherapy could improve the survival rate in ESCC patients with stage II-III who have received surgery.

Investigation of the Mechanism of Traditional Chinese Medicines in Angiogenesis through Network Pharmacology and Data Mining

Although traditional Chinese medicine is effective and safe for the treatment of angiogenesis, the in vivo intervention mechanism is diverse, complex, and largely unknown. Therefore, we aimed to explore the active ingredients of traditional Chinese medicine and their mechanisms of action against angiogenesis. Data on angiogenesis-related targets were collected from GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, DrugBank, and DisGeNET. These were matched to related molecular compounds and ingredients in the traditional Chinese medicine system pharmacology platform. The data were integrated and based on the condition of degree > 1, and relevant literature, target-compound, compound-medicine, and target-compound-medicine networks were constructed using Cytoscape. Molecular docking was used to predict the predominant binding combination of core targets and components. We obtained 79 targets for angiogenesis; 41 targets were matched to 3839 compounds, of which 110 compounds were selected owing to their high correlation with angiogenesis. Fifty-five combinations in the network were obtained by molecular docking, among which PTGS2-astragalin (-9.18 kcal/mol), KDR-astragalin (-7.94 kcal/mol), PTGS2-quercetin (-7.41 kcal/mol), and PTGS2-myricetin (-7.21 kcal/mol) were top. These results indicated that the selected potential core compounds have good binding activity with the core targets. Eighty new combinations were obtained from the network, and the top combinations based on affinity were KDR-beta-carotene (-10.13 kcal/mol), MMP9-beta-sitosterol (-8.04 kcal/mol), MMP9-astragalin (-7.82 kcal/mol), and MMP9-diosgenin (-7.51 kcal/mol). The core targets included PTGS2, KDR, VEGFA, and MMP9. The essential components identified were astragalin, kaempferol, myricetin, quercetin, and β-sitosterol. The crucial Chinese medicines identified included Polygoni Cuspidati Rhizoma et Radix, Morus alba Root Bark, and Forsythiae Fructus. By systematically analysing the ingredients of traditional Chinese medicine and their targets, it is possible to determine their potential mechanisms of action against pathological angiogenesis. Our study provides a basis for further research and the development of new therapeutics for angiogenesis.

Pathway-Based Drug Repurposing with DPNetinfer: A Method to Predict Drug-Pathway Associations via Network-Based Approaches

Identification of drug-pathway associations plays an important role in pathway-based drug repurposing. However, it is time-consuming and costly to uncover new drug-pathway associations experimentally. The drug-induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large-scale prediction of drug-pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug-pathway associations based on substructure-drug-pathway networks via network-based approaches. The results demonstrated that DPNetinfer performed well in a pan-cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway-based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K-Akt pathway. Considering tumor heterogeneity, seven primary site-based models were constructed by DPNetinfer in different drug-pathway networks. In a word, DPNetinfer provides a powerful tool for large-scale prediction of drug-pathway associations in pathway-based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

The global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires an urgent need to find effective therapeutics for the treatment of coronavirus disease 2019 (COVID-19). In this study, we developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 can interact with the nucleocapsid (N) protein of SARS-CoV-2 and is able to suppress the LPS-induced production of several inflammatory cytokines that are highly relevant to the prevention of immunopathology induced by SARS-CoV-2 infection.

Deciphering Pharmacological Mechanism of Buyang Huanwu Decoction for Spinal Cord Injury by Network Pharmacology Approach

Objective

The purpose of this study was to investigate the mechanism of action of the Chinese herbal formula Buyang Huanwu Decoction (BYHWD), which is commonly used to treat nerve injuries, in the treatment of spinal cord injury (SCI) using a network pharmacology method.

Methods

BYHWD-related targets were obtained by mining the TCMSP and BATMAN-TCM databases, and SCI-related targets were obtained by mining the DisGeNET, TTD, CTD, GeneCards, and MalaCards databases. The overlapping targets of the abovementioned targets may be potential therapeutic targets for BYHWD anti-SCI. Subsequently, we performed protein-protein interaction (PPI) analysis, screened the hub genes using Cytoscape software, performed Gene Ontology (GO) annotation and KEGG pathway enrichment analysis, and finally achieved molecular docking between the hub proteins and key active compounds.

Results

The 189 potential therapeutic targets for BYHWD anti-SCI were overlapping targets of 744 BYHWD-related targets and 923 SCI-related targets. The top 10 genes obtained subsequently included AKT1, IL6, MAPK1, TNF, TP53, VEGFA, CASP3, ALB, MAPK8, and JUN. Fifteen signaling pathways were also screened out after enrichment analysis and literature search. The results of molecular docking of key active compounds and hub target proteins showed a good binding affinity for both.

Conclusion

This study shows that BYHWD anti-SCI is characterized by a multicomponent, multitarget, and multipathway synergy and provides new insights to explore the specific mechanisms of BYHWD against SCI.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Identification of the ZEB2 gene as a potential target for epilepsy therapy and the association between rs10496964 and ZEB2 expression

Objective

An association between the rs10496964 polymorphism and the ZEB2 gene has not yet been reported, and the role of ZEB2 in epilepsy therapy is also unclear. The aims of this research were to evaluate the role of ZEB2 in the therapy of epilepsy and to explore the association between rs10496964 and ZEB2 expression.

Methods

We used the expression quantitative trait loci (eQTL) dataset resource from the Brain eQTL Almanac to evaluate the association between rs10496964 and ZEB2 expression in human brain tissue. Pathway and process enrichment analysis, protein–protein interaction analysis, and PhosphoSitePlus® analysis were then performed to further evaluate the role of ZEB2 in the therapy of epilepsy.

Results

The rs10496964 polymorphism was found to regulate the expression of ZEB2 in human brain tissue. The ZEB2 protein interacts with the targets of approved antiepileptic drugs, and a post-translational acetylation modification of ZEB2 was associated with an epilepsy drug therapy.

Conclusion

Our findings suggest that ZEB2 may be involved in the therapy of epilepsy, and rs10496964 regulates ZEB2 expression in human brain tissue.

Network Pharmacology Analysis of ZiShenWan for Diabetic Nephropathy and Experimental Verification of Its Anti-Inflammatory Mechanism

Background

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). The inflammatory response plays a critical role in DN. ZiShenWan (ZSW) is a classical Chinese medicinal formula with remarkable clinical therapeutic effects on DN, but its pharmacological action mechanisms remain unclear.

Aim

In this study, a network pharmacology approach was applied to investigate the pharmacological mechanisms of ZSW in DN therapy. Based on the results of network analysis, the core targets and signaling pathways related to anti-inflammatory effect were verified via experiments in vivo.

Methods

The candidate chemical ingredients of ZSW as well as its putative targets and known therapeutic targets of DN were acquired from appropriate databases. The “herb-ingredient-target” network for ZSW in DN treatment was established. The protein–protein interaction (PPI) network of potential targets was constructed to screen the core targets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. In addition to biochemical and pathological indicators, the core targets and signaling pathways associated with inflammation were partially validated in db/db mice at molecular level.

Results

A total of 56 active ingredients in ZSW and 166 DN-related targets were selected from databases. A high proportion of core targets and top signaling pathways participate in inflammation. ZSW markedly alleviated renal injuries pathologically and regulated related biomarkers. In particular, ZSW significantly inhibited the exaggerated release of inflammatory cytokines such as interleukin (IL)-1β, IL-6, tumor necrosis factor receptor (TNF)-ɑ, and monocyte chemotactic protein (MCP)-1 as well as regulating p38 mitogen-activated protein kinases (MAPK) and phosphoinositide 3-kinase (PI3K)–protein kinase B (Akt) signaling pathways in db/db mice.

Conclusion

This study first comprehensively investigated the active ingredients, potential targets, and molecular mechanism of ZSW as a therapy for DN. ZSW achieved renoprotective effects in DN via regulation of multiple targets and signaling pathways, especially by alleviating inflammation. Results indicate that ZSW is a promising multi-target therapeutic approach for DN treatment.

Targeting pan-essential genes in cancer: Challenges and opportunities

Despite remarkable successes in the clinic, cancer targeted therapy development remains challenging and the failure rate is disappointingly high. This problem is partly due to the misapplication of the targeted therapy paradigm to therapeutics targeting pan-essential genes, which can result in therapeutics whereby efficacy is attenuated by dose-limiting toxicity. Here we summarize the key features of successful chemotherapy and targeted therapy agents, and use case studies to outline recurrent challenges to drug development efforts targeting pan-essential genes. Finally, we suggest strategies to avoid previous pitfalls for ongoing and future development of pan-essential therapeutics.

Diagnostic and therapeutic values of PMEPA1 and its correlation with tumor immunity in pan-cancer

AIMS

The prostate transmembrane protein, androgen induced 1 (PMEPA1) is differentially expressed in pan-cancer. However, PMEPA1 specific role in cancers has not been fully clarified. This study aims to explore the potential role of Pmepa1 in pan-cancer and specific cancer, with a view to deepening the research on the pathological mechanism of cancer.

MAIN METHODS

The Perl language and R language were used to identify the correlation between PMEPA1 expression level and clinical indicators, prognosis values, tumor microenvironment, immune cells' infiltration, immune checkpoint genes, TMB and MSI. The Therapeutic Target Database was used for identifying potential therapeutic drugs that target the pathways that are significantly affected by PMEPA1 expression.

KEY FINDINGS

PMEPA1 differential expression significantly correlated with patients' age, race, tumors' stage and status. PMEPA1 high expression was closely correlated with poor prognosis in many cancer types, excluding prostate adenocarcinoma. PMEPA1 expression was closely related to tumor cells and the immune microenvironment in stromal and immune cells' level, immune cells' infiltration, immune checkpoint genes, tumor mutational burden and microsatellite instability. We also found that the activity of the olfactory transduction pathway was closely related to PMEPA1 expression. In pan-cancer, Trifluoperazine and Halofantrine have the potential to reduce PMEPA1 expression.

SIGNIFICANCE

This study integrated existing data to explore PMEPA1 potential function in cancers, provided insights for the future cancer-related studies.

Exploring the Pharmacological Mechanism of Duhuo Jisheng Decoction in Treating Osteoporosis Based on Network Pharmacology

Objective

The purpose of this work is to study the mechanism of action of Duhuo Jisheng Decoction (DHJSD) in the treatment of osteoporosis based on the methods of bioinformatics and network pharmacology.

Methods

In this study, the active compounds of each medicinal ingredient of DHJSD and their corresponding targets were obtained from TCMSP database. Osteoporosis was treated as search query in GeneCards, MalaCards, DisGeNET, Therapeutic Target Database (TTD), Comparative Toxicogenomics Database (CTD), and OMIM databases to obtain disease-related genes. The overlapping targets of DHJSD and osteoporosis were identified, and then GO and KEGG enrichment analysis were performed. Cytoscape was employed to construct DHJSD-compounds-target genes-osteoporosis network and protein-protein interaction (PPI) network. CytoHubba was utilized to select the hub genes. The activities of binding of hub genes and key components were confirmed by molecular docking.

Results

174 active compounds and their 205 related potential targets were identified in DHJSD for the treatment of osteoporosis, including 10 hub genes (AKT1, ALB, IL6, MAPK3, VEGFA, JUN, CASP3, EGFR, MYC, and EGF). Pathway enrichment analysis of target proteins indicated that osteoclast differentiation, AGE-RAGE signaling pathway in diabetic complications, Wnt signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, calcium signaling pathway, and TNF signaling pathway were the specifically major pathways regulated by DHJSD against osteoporosis. Further verification based on molecular docking results showed that the small molecule compounds (Quercetin, Kaempferol, Beta-sitosterol, Beta-carotene, and Formononetin) contained in DHJSD generally have excellent binding affinity to the macromolecular target proteins encoded by the top 10 genes.

Conclusion

This study reveals the characteristics of multi-component, multi-target, and multi-pathway of DHJSD against osteoporosis and provides novel insights for verifying the mechanism of DHJSD in the treatment of osteoporosis.

A small molecule compound berberine as an orally active therapeutic candidate against COVID-19 and SARS: A computational and mechanistic study

The novel coronavirus disease, COVID-19, has grown into a global pandemic and a major public health threat since its breakout in December 2019. To date, no specific therapeutic drug or vaccine for treating COVID-19 and SARS has been FDA approved. Previous studies suggest that berberine, an isoquinoline alkaloid, has shown various biological activities that may help against COVID-19 and SARS, including antiviral, anti-allergy and inflammation, hepatoprotection against drug- and infection-induced liver injury, as well as reducing oxidative stress. In particular, berberine has a wide range of antiviral activities such as anti-influenza, anti-hepatitis C, anti-cytomegalovirus, and anti-alphavirus. As an ingredient recommended in guidelines issued by the China National Health Commission for COVID-19 to be combined with other therapy, berberine is a promising orally administered therapeutic candidate against SARS-CoV and SARS-CoV-2. The current study comprehensively evaluates the potential therapeutic mechanisms of berberine in preventing and treating COVID-19 and SARS using computational modeling, including target mining, gene ontology enrichment, pathway analyses, protein-protein interaction analysis, and in silico molecular docking. An orally available immunotherapeutic-berberine nanomedicine, named NIT-X, has been developed by our group and has shown significantly increased oral bioavailability of berberine, increased IFN-γ production by CD8+ T cells, and inhibition of mast cell histamine release in vivo, suggesting a protective immune response. We further validated the inhibition of replication of SARS-CoV-2 in lung epithelial cells line in vitro (Calu3 cells) by berberine. Moreover, the expression of targets including ACE2, TMPRSS2, IL-1α, IL-8, IL-6, and CCL-2 in SARS-CoV-2 infected Calu3 cells were significantly suppressed by NIT-X. By supporting protective immunity while inhibiting pro-inflammatory cytokines; inhibiting viral infection and replication; inducing apoptosis; and protecting against tissue damage, berberine is a promising candidate in preventing and treating COVID-19 and SARS. Given the high oral bioavailability and safety of berberine nanomedicine, the current study may lead to the development of berberine as an orally, active therapeutic against COVID-19 and SARS.

The Main Alkaloids in Uncaria rhynchophylla and Their Anti-Alzheimer's Disease Mechanism Determined by a Network Pharmacology Approach

Alzheimer's disease (AD) is a growing concern in modern society, and effective drugs for its treatment are lacking. Uncaria rhynchophylla (UR) and its main alkaloids have been studied to treat neurodegenerative diseases such as AD. This study aimed to uncover the key components and mechanism of the anti-AD effect of UR alkaloids through a network pharmacology approach. The analysis identified 10 alkaloids from UR based on HPLC that corresponded to 90 anti-AD targets. A potential alkaloid target-AD target network indicated that corynoxine, corynantheine, isorhynchophylline, dihydrocorynatheine, and isocorynoxeine are likely to become key components for AD treatment. KEGG pathway enrichment analysis revealed the Alzheimers disease (hsa05010) was the pathway most significantly enriched in alkaloids against AD. Further analysis revealed that 28 out of 90 targets were significantly correlated with Aβ and tau pathology. These targets were validated using a Gene Expression Omnibus (GEO) dataset. Molecular docking studies were carried out to verify the binding of corynoxine and corynantheine to core targets related to Aβ and tau pathology. In addition, the cholinergic synapse (hsa04725) and dopaminergic synapse (hsa04728) pathways were significantly enriched. Our findings indicate that UR alkaloids directly exert an AD treatment effect by acting on multiple pathological processes in AD.

Epigenetic Target Fishing with Accurate Machine Learning Models

Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents many structure-activity relationships that have not been exploited thus far to develop predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26 318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. We built predictive models with high accuracy for small molecules' epigenetic target profiling through a systematic comparison of the machine learning models trained on different molecular fingerprints. The models were thoroughly validated, showing mean precisions of up to 0.952 for the epigenetic target prediction task. Our results indicate that the models reported herein have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as a freely accessible web application.

SAveRUNNER: an R-based tool for drug repurposing

BACKGROUND

Currently, no proven effective drugs for the novel coronavirus disease COVID-19 exist and despite widespread vaccination campaigns, we are far short from herd immunity. The number of people who are still vulnerable to the virus is too high to hamper new outbreaks, leading a compelling need to find new therapeutic options devoted to combat SARS-CoV-2 infection. Drug repurposing represents an effective drug discovery strategy from existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery.

RESULTS

We developed a network-based tool for drug repurposing provided as a freely available R-code, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), with the aim to offer a promising framework to efficiently detect putative novel indications for currently marketed drugs against diseases of interest. SAveRUNNER predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-associated proteins in the human interactome through the computation of a novel network-based similarity measure, which prioritizes associations between drugs and diseases located in the same network neighborhoods.

CONCLUSIONS

The algorithm was successfully applied to predict off-label drugs to be repositioned against the new human coronavirus (2019-nCoV/SARS-CoV-2), and it achieved a high accuracy in the identification of well-known drug indications, thus revealing itself as a powerful tool to rapidly detect potential novel medical indications for various drugs that are worth of further investigation. SAveRUNNER source code is freely available at https://github.com/giuliafiscon/SAveRUNNER.git , along with a comprehensive user guide.

The miRNA: a small but powerful RNA for COVID-19

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

Virus-CKB: an integrated bioinformatics platform and analysis resource for COVID-19 research

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is an urgent need for medicines that can help before vaccines are available. In this study, we present a viral-associated disease-specific chemogenomics knowledgebase (Virus-CKB) and apply our computational systems pharmacology-target mapping to rapidly predict the FDA-approved drugs which can quickly progress into clinical trials to meet the urgent demand of the COVID-19 outbreak. Virus-CKB reuses the underlying platform of our DAKB-GPCRs but adds new features like multiple-compound support, multi-cavity protein support and customizable symbol display. Our one-stop computing platform describes the chemical molecules, genes and proteins involved in viral-associated diseases regulation. To date, Virus-CKB archived 65 antiviral drugs in the market, 107 viral-related targets with 189 available 3D crystal or cryo-EM structures and 2698 chemical agents reported for these target proteins. Moreover, Virus-CKB is implemented with web applications for the prediction of the relevant protein targets and analysis and visualization of the outputs, including HTDocking, TargetHunter, BBB predictor, NGL Viewer, Spider Plot, etc. The Virus-CKB server is accessible at https://www.cbligand.org/g/virus-ckb.

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.

Assessing the Function of the ZFP90 Variant rs1170426 in SLE and the Association Between SLE Drug Target and Susceptibility Genes

A genome-wide association study (GWAS) has discovered that a polymorphism in the ZFP90 gene is associated with systemic lupus erythematosus (SLE). In this study, we explored the candidate function of a ZFP90 variant (rs1170426) in the context of SLE and detected the relationship between SLE susceptible genes and SLE drug target genes. First, we investigated the regulatory role of rs1170426 on ZFP90 expression by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells (PBMCs), T, B, and monocytes cells and annotated the regulatory function of rs1170426 using bioinformatic databases. Second, we compared the case-control difference in ZFP90 expression levels. Third, we analyzed the association of genotype and ZFP90 expression levels with SLE clinical characters. Last, we showed the interaction of SLE susceptibility genes with SLE drug target genes. Subjects with the risk allele “C” of rs1170426 had lower expression levels of ZFP90 in PBMCs (P = 0.006) and CD8+ T cells (P = 0.003) from controls. SLE cases also had lower expression levels compared with controls (P = 2.78E-9). After correction for multiple testing, the ZFP90 expression levels were related to serositis (FDR p = 0.004), arthritis (FDR p = 0.020), hematological involvement (FDR p = 0.021), and increased C-reactive protein (CRP) (FDR p = 0.005) in cases. Furthermore, the SLE susceptible genes and the recognized SLE drug target genes were more likely to act upon each other compared with non-SLE genetic genes (OR = 2.701, P = 1.80E-5). These findings suggest that ZFP90 might play a role in the pathogenesis of SLE, and SLE genetics would contribute to therapeutic drug discovery.

The Relevance of Bioinformatics Applications in the Discovery of Vaccine Candidates and Potential Drugs for COVID-19 Treatment

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

A review on compound-protein interaction prediction methods: Data, format, representation and model

There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.

A Network Pharmacology to Explore the Mechanism of Calculus Bovis in the Treatment of Ischemic Stroke

BACKGROUND

Calculus Bovis is a valuable Chinese medicine, which is widely used in the clinical treatment of ischemic stroke. The present study is aimed at investigating its target and the mechanism involved in ischemic stroke treatment by network pharmacology.

METHODS

Effective compounds of Calculus Bovis were collected using methods of network pharmacology and using the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Potential compound targets were searched in the TCMSP and SwissTargetPrediction databases. Ischemic stroke-related disease targets were searched in the Drugbank, DisGeNet, OMIM, and TTD databases. These two types of targets were uploaded to the STRING database, and a network of their interaction (PPI) was built with its characteristics calculated, aiming to reveal a number of key targets. Hub genes were selected using a plug-in of the Cytoscape software, and Gene Ontology (GO) biological processes and pathway enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted using the clusterProfiler package of R language.

RESULTS

Among 12 compounds, deoxycorticosterone, methyl cholate, and biliverdin were potentially effective components. A total of 344 Calculus Bovis compound targets and 590 ischemic stroke targets were found with 92 overlapping targets, including hub genes such as TP53, AKT, PIK2CA, MAPK3, MMP9, and MMP2. Biological functions of Calculus Bovis are associated with protein hydrolyzation, phosphorylation of serine/threonine residues of protein substrates, peptide bond hydrolyzation of peptides and proteins, hydrolyzation of intracellular second messengers, antioxidation and reduction, RNA transcription, and other biological processes.

CONCLUSION

Calculus Bovis may play a role in ischemic stroke by activating PI3K-AKT and MAPK signaling pathways, which are involved in regulating inflammatory response, cell apoptosis, and proliferation.

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus

The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks

KEY MESSAGE

We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (sgRNA) plays an important role in gene redirection and editing. sgRNA has played an important role in the improvement of agronomic species, but there is a lack of effective bioinformatics tools to identify the activity of sgRNA in agronomic species. Therefore, it is necessary to develop a method based on machine learning to identify sgRNA high on-target activity. In this work, we proposed a simple convolutional neural network method to identify sgRNA high on-target activity. Our study used one-hot encoding and k-mers for sequence data conversion and a voting algorithm for constructing the convolutional neural network ensemble model sgRNACNN for the prediction of sgRNA activity. The ensemble model sgRNACNN was used for predictions in four crops: Glycine max, Zea mays, Sorghum bicolor and Triticum aestivum. The accuracy rates of the four crops in the sgRNACNN model were 82.43%, 80.33%, 78.25% and 87.49%, respectively. The experimental results showed that sgRNACNN realizes the identification of high on-target activity sgRNA of agronomic data and can meet the demands of sgRNA activity prediction in agronomy to a certain extent. These results have certain significance for guiding crop gene editing and academic research. The source code and relevant dataset can be found in the following link: https://github.com/nmt315320/sgRNACNN.git .

Mechanism of Modified Danggui Sini Decoction for Knee Osteoarthritis Based on Network Pharmacology and Molecular Docking

Objective

This study aimed to explore the mechanism of Modified Danggui Sini Decoction in the treatment of knee osteoarthritis via a combination of network pharmacology and molecular docking.

Methods

The main chemical components and corresponding targets of Modified Danggui Sini Decoction were searched and screened in TCMSP database. The disease targets of knee osteoarthritis were summarized in GeneCards, OMIM, PharmGkb, TTD, and DrugBank databases. The visual interactive network of “drugs-active components-disease targets” was drawn by Cytoscape 3.8.1 software. The protein-protein interaction network was constructed by STRING database. Then, GO function and KEGG pathway enrichment were analyzed by Bioconductor/R, and the pathway of the highest degree of correlation with knee osteoarthritis was selected for specific analysis. Finally, molecular docking was used to screen and verify core genes by AutoDockTools software.

Results

Seventy-one main components of Modified Danggui Sini Decoction and 116 potential therapeutic targets of knee osteoarthritis were selected. The KEGG pathway and the GO function enrichment analysis showed that the targets of Modified Danggui Sini Decoction in the treatment of knee osteoarthritis were mainly concentrated on PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, apoptosis signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation signaling pathway, HIF-1 signaling pathway, and NF-κB signaling pathway. It mainly involved inflammatory reaction, regulation of apoptotic signaling pathway, cellular response to regulation of inflammatory response, cellular response to oxidative stress, and other biological processes. The molecular docking results showed that ESR1-wogonin, MAPK1-quercetin, RELA-wogonin, RELA-baicalein, TP53-baicalein, TP53-quercetin, and RELA-quercetin have strong docking activities.

Conclusion

Modified Danggui Sini Decoction has the hierarchical network characteristics of “multicomponent, multitarget, multifunction, and multipathway” in the treatment of knee osteoarthritis. It mainly regulates the proliferation and apoptosis of chondrocytes by regulating the PI3K-Akt signaling pathway and establishes cross-talk with many downstream inflammatory-related pathways to reduce the overall inflammatory response. Meanwhile, HIF-1 expression was used to ensure the normal function and metabolism of knee joint under hypoxia condition, and the above processes play a key role in the treatment of knee osteoarthritis.

Open Science Resources for the Mass Spectrometry-Based Analysis of SARS-CoV-2

The SARS-CoV-2 virus is the causative agent of the 2020 pandemic leading to the COVID-19 respiratory disease. With many scientific and humanitarian efforts ongoing to develop diagnostic tests, vaccines, and treatments for COVID-19, and to prevent the spread of SARS-CoV-2, mass spectrometry research, including proteomics, is playing a role in determining the biology of this viral infection. Proteomics studies are starting to lead to an understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein-protein interactions, and post-translational modifications. This is beginning to provide insights into potential therapeutic targets or diagnostic strategies that can be used to reduce the long-term burden of the pandemic. However, the extraordinary situation caused by the global pandemic is also highlighting the need to improve mass spectrometry data and workflow sharing. We therefore describe freely available data and computational resources that can facilitate and assist the mass spectrometry-based analysis of SARS-CoV-2. We exemplify this by reanalyzing a virus-host interactome data set to detect protein-protein interactions and identify host proteins that could potentially be used as targets for drug repurposing.

Knowledge-Guided "Community Network" Analysis Reveals the Functional Modules and Candidate Targets in Non-Small-Cell Lung Cancer

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies that are the leading cause of cancer-related death worldwide. Although many NSCLC-related genes and pathways have been identified, there remains an urgent need to mechanistically understand how these genes and pathways drive NSCLC. Here, we propose a knowledge-guided and network-based integration method, called the node and edge Prioritization-based Community Analysis, to identify functional modules and their candidate targets in NSCLC. The protein–protein interaction network was prioritized by performing a random walk with restart algorithm based on NSCLC seed genes and the integrating edge weights, and then a “community network” was constructed by combining Girvan–Newman and Label Propagation algorithms. This systems biology analysis revealed that the CCNB1-mediated network in the largest community provides a modular biomarker, the second community serves as a drug regulatory module, and the two are connected by some contextual signaling motifs. Moreover, integrating structural information into the signaling network suggested novel protein–protein interactions with therapeutic significance, such as interactions between GNG11 and CXCR2, CXCL3, and PPBP. This study provides new mechanistic insights into the landscape of cellular functions in the context of modular networks and will help in developing therapeutic targets for NSCLC.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

SAveRUNNER: A network-based algorithm for drug repurposing and its application to COVID-19

The novelty of new human coronavirus COVID-19/SARS-CoV-2 and the lack of effective drugs and vaccines gave rise to a wide variety of strategies employed to fight this worldwide pandemic. Many of these strategies rely on the repositioning of existing drugs that could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we presented a new network-based algorithm for drug repositioning, called SAveRUNNER (Searching off-lAbel dRUg aNd NEtwoRk), which predicts drug-disease associations by quantifying the interplay between the drug targets and the disease-specific proteins in the human interactome via a novel network-based similarity measure that prioritizes associations between drugs and diseases locating in the same network neighborhoods. Specifically, we applied SAveRUNNER on a panel of 14 selected diseases with a consolidated knowledge about their disease-causing genes and that have been found to be related to COVID-19 for genetic similarity (i.e., SARS), comorbidity (e.g., cardiovascular diseases), or for their association to drugs tentatively repurposed to treat COVID-19 (e.g., malaria, HIV, rheumatoid arthritis). Focusing specifically on SARS subnetwork, we identified 282 repurposable drugs, including some the most rumored off-label drugs for COVID-19 treatments (e.g., chloroquine, hydroxychloroquine, tocilizumab, heparin), as well as a new combination therapy of 5 drugs (hydroxychloroquine, chloroquine, lopinavir, ritonavir, remdesivir), actually used in clinical practice. Furthermore, to maximize the efficiency of putative downstream validation experiments, we prioritized 24 potential anti-SARS-CoV repurposable drugs based on their network-based similarity values. These top-ranked drugs include ACE-inhibitors, monoclonal antibodies (e.g., anti-IFNγ, anti-TNFα, anti-IL12, anti-IL1β, anti-IL6), and thrombin inhibitors. Finally, our findings were in-silico validated by performing a gene set enrichment analysis, which confirmed that most of the network-predicted repurposable drugs may have a potential treatment effect against human coronavirus infections.

Network Pharmacology-Based Analysis on the Mechanism of Action of Ephedrae Herba-Cinnamomi Ramulus Couplet Medicines in the Treatment for Psoriasis

Background

This study explored the mechanism of action of Ephedrae Herba-Cinnamomi Ramulus couplet medicine (MGCM) at the pharmacological level in the treatment of psoriasis.

Material/Methods

The active ingredients in MGCM were mined through literature retrieval and the BATMAN-TCM database, and potential targets were predicted. In addition, targets associated with psoriasis were acquired using multiple disease-related databases. Thereafter, an interaction network between candidate MGCM targets and the known psoriasis-associated targets was constructed based on the protein–protein interaction (PPI) data, using the STRING database. Then, the topological parameter degree was determined for mining the core targets for MGCM in the treatment of psoriasis, which also represented the major hubs within the PPI network. In addition, the core networks of targets and ingredients were constructed using Cytoscape software to apply MGCM in the treatment for psoriasis. These core targets were then analyzed for Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathway enrichment using OmicShare.

Results

The ingredient-target core network of MGCM for treating psoriasis was constructed; it contained 52 active ingredients and corresponded to 19 core targets. In addition, based on enrichment analysis, these core targets were majorly enriched for several biological processes (immuno-inflammatory responses, leukocyte differentiation, energy metabolism, angiogenesis, and programmed cell death) together with the relevant pathways (Janus kinase-signal transducer and activator of transcription, toll-like receptors, nuclear factor κB, vascular endothelial growth factor, and peroxisome proliferator-activated receptor), thus identifying the possible mechanism of action of MGCM in treating psoriasis.

Conclusions

The present network pharmacology study indicated that MGCM alleviates various pathological factors of psoriasis through multiple compounds, multiple targets, and multiple pathways.

HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine

Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.

Exploring the Active Components of Simotang Oral Liquid and Their Potential Mechanism of Action on Gastrointestinal Disorders by Integrating Ultrahigh-Pressure Liquid Chromatography Coupled with Linear Ion Trap-Orbitrap Analysis and Network Pharmacology

Simotang oral liquid (SMT), a well-known traditional Chinese medicine formula composed of four medicinal and edible plants, has been extensively used for treating gastrointestinal disorders (GIDs) since ancient times. However, the major active constituents and the underlying molecular mechanism of SMT on GIDs are still partially understood. Herein, the preliminary chemical profile of SMT was first identified by ultrahigh-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap). In total, 70 components were identified. Then, a network pharmacology approach integrating target prediction, pathway enrichment analysis, and network construction was adopted to explore the therapeutic mechanism of SMT. As a result, 170 main targets were screened out and considered as effective players in ameliorating GIDs. More importantly, the major hubs were found to be highly enriched in a calcium signaling pathway. Furthermore, 26 core SMT-related genes were identified, which may play key roles in ameliorating gastrointestinal motility. In conclusion, this work would provide valuable information for further development and clinical application of SMT.

MASI: microbiota-active substance interactions database

Xenobiotic and host active substances interact with gut microbiota to influence human health and therapeutics. Dietary, pharmaceutical, herbal and environmental substances are modified by microbiota with altered bioavailabilities, bioactivities and toxic effects. Xenobiotics also affect microbiota with health implications. Knowledge of these microbiota and active substance interactions is important for understanding microbiota-regulated functions and therapeutics. Established microbiota databases provide useful information about the microbiota-disease associations, diet and drug interventions, and microbiota modulation of drugs. However, there is insufficient information on the active substances modified by microbiota and the abundance of gut bacteria in humans. Only ∼7% drugs are covered by the established databases. To complement these databases, we developed MASI, Microbiota—Active Substance Interactions database, for providing the information about the microbiota alteration of various substances, substance alteration of microbiota, and the abundance of gut bacteria in humans. These include 1,051 pharmaceutical, 103 dietary, 119 herbal, 46 probiotic, 142 environmental substances interacting with 806 microbiota species linked to 56 diseases and 784 microbiota–disease associations. MASI covers 11 215 bacteria-pharmaceutical, 914 bacteria-herbal, 309 bacteria-dietary, 753 bacteria-environmental substance interactions and the abundance profiles of 259 bacteria species in 3465 patients and 5334 healthy individuals. MASI is freely accessible at http://www.aiddlab.com/MASI.

GIMICA: host genetic and immune factors shaping human microbiota

Besides the environmental factors having tremendous impacts on the composition of microbial community, the host factors have recently gained extensive attentions on their roles in shaping human microbiota. There are two major types of host factors: host genetic factors (HGFs) and host immune factors (HIFs). These factors of each type are essential for defining the chemical and physical landscapes inhabited by microbiota, and the collective consideration of both types have great implication to serve comprehensive health management. However, no database was available to provide the comprehensive factors of both types. Herein, a database entitled 'Host Genetic and Immune Factors Shaping Human Microbiota (GIMICA)' was constructed. Based on the 4257 microbes confirmed to inhabit nine sites of human body, 2851 HGFs (1368 single nucleotide polymorphisms (SNPs), 186 copy number variations (CNVs), and 1297 non-coding ribonucleic acids (RNAs)) modulating the expression of 370 microbes were collected, and 549 HIFs (126 lymphocytes and phagocytes, 387 immune proteins, and 36 immune pathways) regulating the abundance of 455 microbes were also provided. All in all, GIMICA enabled the collective consideration not only between different types of host factor but also between the host and environmental ones, which is freely accessible without login requirement at: https://idrblab.org/gimica/.

CMNPD: a comprehensive marine natural products database towards facilitating drug discovery from the ocean

Marine organisms are expected to be an important source of inspiration for drug discovery after terrestrial plants and microorganisms. Despite the remarkable progress in the field of marine natural products (MNPs) chemistry, there are only a few open access databases dedicated to MNPs research. To meet the growing demand for mining and sharing for MNPs-related data resources, we developed CMNPD, a comprehensive marine natural products database based on manually curated data. CMNPD currently contains more than 31 000 chemical entities with various physicochemical and pharmacokinetic properties, standardized biological activity data, systematic taxonomy and geographical distribution of source organisms, and detailed literature citations. It is an integrated platform for structure dereplication (assessment of novelty) of (marine) natural products, discovery of lead compounds, data mining of structure-activity relationships and investigation of chemical ecology. Access is available through a user-friendly web interface at https://www.cmnpd.org. We are committed to providing a free data sharing platform for not only professional MNPs researchers but also the broader scientific community to facilitate drug discovery from the ocean.

Study of the active ingredients and mechanism of Sparganii rhizoma in gastric cancer based on HPLC-Q-TOF-MS/MS and network pharmacology

Sparganii rhizoma (SL) has potential therapeutic effects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF–MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identified compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identified from the aqueous extract of SL. Through network analysis, we identified seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, caffeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K-Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC.

COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics

The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists ∼130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), ∼40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

INTEDE: interactome of drug-metabolizing enzymes

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.