DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes

Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

Aim:

To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations.

Background:

Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell.

Objective:

To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data.

Method:

We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with

Result:

To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers.

Conclusion:

Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

Pathway Maps of Orphan and Complex Diseases Using an Integrative Computational Approach

Orphan diseases (ODs) are progressive genetic disorders, which affect a small number of people. The principal fundamental aspects related to these diseases include insufficient knowledge of mechanisms involved in the physiopathology necessary to access correct diagnosis and to develop appropriate healthcare. Unlike ODs, complex diseases (CDs) have been widely studied due to their high incidence and prevalence allowing to understand the underlying mechanisms controlling their physiopathology. Few studies have focused on the relationship between ODs and CDs to identify potential shared pathways and related molecular mechanisms which would allow improving disease diagnosis, prognosis, and treatment. We have performed a computational approach to studying CDs and ODs relationships through (1) connecting diseases to genes based on genes-diseases associations from public databases, (2) connecting ODs and CDs through binary associations based on common associated genes, and (3) linking ODs and CDs to common enriched pathways. Among the most shared significant pathways between ODs and CDs, we found pathways in cancer, p53 signaling, mismatch repair, mTOR signaling, B cell receptor signaling, and apoptosis pathways. Our findings represent a reliable resource that will contribute to identify the relationships between drugs and disease-pathway networks, enabling to optimise patient diagnosis and disease treatment.

Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm

In recent years, latent metabolite-disease associations have been a significant focus in the biomedical domain. And more and more experimental evidence has been adduced that metabolites correlate with the diagnosis of complex human diseases. Several computational methods have been developed to detect potential metabolite-disease associations. In this article, we propose a novel method based on the spy strategy and an artificial bee colony (ABC) algorithm for metabolite-disease association prediction (SSABCMDA). Due to the fact that there are large parts of missing associations in unconfirmed metabolite-disease pairs, spy strategy is adopted to extract reliable negative samples from unconfirmed pairs. Considering the effects of parameters, the ABC algorithm is utilized to optimize parameters. In relevant cross-validation experiments, our method achieves excellent predictive performance. Moreover, three types of case studies are conducted on three common diseases to demonstrate the validity and utility of SSABCMDA method. Relevant experimental results indicate that our method can predict potential associations between metabolites and diseases effectively.

A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

PDmethDB: A curated Parkinson's disease associated methylation information database

Parkinson's disease (PD) is the second most common neurodegenerative disease, of which the histopathological hallmark is the formation of Lewy bodies consisting of α-synuclein as the major component. α-Synuclein can sequester DNA Methyltransferase 1 (DNMT1), the maintenance DNA methylation enzyme, from the nucleus and into the cytoplasm, leading to global DNA hypomethylation in human brain. As DNA methylation is a major epigenetic modification that regulates gene expression and there is no specific database storing PD associated methylation information, PDmethDB (Parkinson's Disease Methylation Database) aims to curate PD associated methylation information from literature to facilitate the study of the relationship between PD and methylation. Currently, PDmethDB contains 97,077 PD methylation associated entries among 12,308 molecules, 37,944 CpG sites, 31 tissues and 3 species through a review of about 1600 published papers. This includes information concerning the gene/molecule name, CpG site, methylation alteration, expression alteration, tissue, PMID, experimental method, and a brief description about the entry. PDmethDB provides a user-friendly interface to search, browse, download and submit data. PDmethDB supports browsing by molecule, species, tissue, gene region, methylation alteration and experimental methods. PDmethDB also shows the entry gene interaction network including protein-protein interactions and miRNA-targets interactions with a highlight of PD associated genes from DisGeNET database. PDmethDB aims to facilitate the understanding of the relationship between PD and methylation. Database URL: https://ageing.shinyapps.io/pdmethdb/.

Genetic Polymorphism of ADORA2A Is Associated With the Risk of Epilepsy and Predisposition to Neurologic Comorbidity in Chinese Southern Children

Epilepsy, a common disorder of the brain, exhibits a high morbidity rate in children. Childhood epilepsy (CE) is frequently comorbid with neurologic and developmental disorders, sharing underlying genetic factors. This study aimed to investigate the impact of ADORA2A, BDNF, and NTRK2 gene polymorphisms on the risk of childhood epilepsy and their associations with predisposition to epileptic comorbidities. A total of 444 children were enrolled in this study, and three single nucleotide polymorphisms, including ADORA2A rs2298383, BDNF rs6265, and NTRK2 rs1778929, were genotyped. The frequency distribution of genotypes was compared not only between CE patients and healthy children but also between CE patients with and without comorbidities. The results indicated that the carriers of ADORA2A rs2298383 TT genotype tended to have a lower risk of epilepsy (OR = 0.48, 95% CI = 0.30–0.76), while the CT genotype was related to a higher risk (OR = 1.56, 95% CI = 1.06–2.27). The ADORA2A rs2298383 CC genotype predisposed CE patients to comorbid neurologic disorders (OR = 2.76, 95% CI = 1.31–5.80). Genetic variations in BDNF rs6265 and NTRK2 rs1778929 had no significant association with CE and its comorbidities. Fourteen ADORA2A target genes related to epilepsy were identified by the protein–protein interaction analysis, which were mainly involved in the biological processes of “negative regulation of neuron death” and “purine nucleoside biosynthetic process” through the gene functional enrichment analysis. Our study revealed that the genetic polymorphism of ADORA2A rs2298383 was associated with CE risk and predisposition to neurologic comorbidity in children with epilepsy, and the involved mechanism might be related to the regulation of neuron death and purine nucleoside biosynthesis.

Revealing the Pharmacological Mechanism of Acorus tatarinowii in the Treatment of Ischemic Stroke Based on Network Pharmacology

Aim

Stroke is the second significant cause for death, with ischemic stroke (IS) being the main type threatening human being's health. Acorus tatarinowii (AT) is widely used in the treatment of Alzheimer disease, epilepsy, depression, and stroke, which leads to disorders of consciousness disease. However, the systemic mechanism of AT treating IS is unexplicit. This article is supposed to explain why AT has an effect on the treatment of IS in a comprehensive and systematic way by network pharmacology.

Methods and Materials

ADME (absorbed, distributed, metabolized, and excreted) is an important property for screening-related compounds in AT, which were screening out of TCMSP, TCMID, Chemistry Database, and literature from CNKI. Then, these targets related to screened compounds were predicted via Swiss Targets, when AT-related targets database was established. The gene targets related to IS were collected from DisGeNET and GeneCards. IS-AT is a common protein interactive network established by STRING Database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analysed by IS-AT common target genes. Cytoscape software was used to establish a visualized network for active compounds-core targets and core target proteins-proteins interactive network. Furthermore, we drew a signal pathway picture about its effect to reveal the basic mechanism of AT against IS systematically.

Results

There were 53 active compounds screened from AT, inferring the main therapeutic substances as follows: bisasaricin, 3-cyclohexene-1-methanol-α,α,4-trimethyl,acetate, cis,cis,cis-7,10,13-hexadecatrienal, hydroxyacoronene, nerolidol, galgravin, veraguensin, 2′-o-methyl isoliquiritigenin, gamma-asarone, and alpha-asarone. We obtained 398 related targets, 63 of which were the same as the IS-related genes from targets prediction. Except for GRM2, remaining 62 target genes have an interactive relation, respectively. The top 10 degree core target genes were IL6, TNF, IL1B, TLR4, NOS3, MAPK1, PTGS2, VEGFA, JUN, and MMP9. There were more than 20 terms of biological process, 7 terms of cellular components, and 14 terms of molecular function through GO enrichment analysis and 13 terms of signal pathway from KEGG enrichment analysis based on P < 0.05.

Conclusion

AT had a therapeutic effect for ischemic via multicomponent, multitarget, and multisignal pathway, which provided a novel research aspect for AT against IS.

Supervised learning is an accurate method for network-based gene classification

Background

Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem.

Results

In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene’s full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation’s appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows.

Availability and implementation

The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available.

Contact

arjun@msu.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

Chinese herbal medicine promote tissue differentiation in colorectal cancer by activating HSD11B2

BACKGROUND

Colorectal cancer is a common malignant tumor of the digestive tract. In recent years, the incidence rate has increased year by year and is showing a younger trend. The application of Chinese herbal medicine (CHM) is one of the important methods for the treatment of colorectal cancer. CHM refers to the main therapeutic drugs based on traditional Chinese medicine (TCM), which is still valued. Many effective anticancer small-molecule compounds are derived from CHMs, and their effective anticancer ingredients and targets must be clarified and to further understand the molecular mechanisms by which CHM affects cancer.

METHODS

We analyzed the ingredients in CHM that were found to be effective against colorectal cancer and constructed an interaction network among these ingredients and the target protein. By analyzing the number of connections in the network and their type of interaction, we identified the key target protein Corticosteroid 11-beta-dehydrogenase isozyme 2, the enzyme encoded by HSD11B2. Analyses of HSD11B2 expression, survival curve, and co-expressed genes helped clarify the correlation between HSD11B2 and colorectal cancer as well as its underlying molecular mechanism.

RESULTS

We determined that the anticancer ingredients contained in Sanguisorba officinalis, Patrinia scabiosaefolia, and Smilax china had more connections to the target proteins found in colorectal cancer. In the interaction network, eight small-molecule compounds had an activating effect on HSD11B2. The expression of the HSD11B2 was markedly decreased in colorectal cancer tissues and was positively correlated with the overall survival time of patients. In addition, co-expression analyses showed a close relationship between HSD11B2 and tissue-specific genes in colorectal tissues. The expression levels of HSD11B2 in well-, moderately, and poorly differentiated tissues progressively decreased.

CONCLUSION

The HSD11B2 protein was a key CHM target for treating colorectal cancer. The key role of CHM may lie in activating HSD11B2 and further promoting tissue differentiation in colorectal cancer.

Discover novel disease-associated genes based on regulatory networks of long-range chromatin interactions

Identifying genes and non-coding genetic variants that are genetically associated with complex diseases and the underlying mechanisms is one of the most important questions in functional genomics. Due to the limited statistical power and the lack of mechanistic modeling, traditional genome-wide association studies (GWAS) is restricted to fully address this question. Based on multi-omics data integration, cell-type specific regulatory networks can be built to improve GWAS analysis. In this study, we developed a new computational infrastructure, APRIL, to incorporate 3D chromatin interactions into regulatory network construction, which can extend the networks to include long-range cis-regulatory links between non-coding GWAS SNPs and target genes. Combinatorial transcription factors that co-regulate groups of genes are also inferred to further expand the networks with trans-regulation. A suite of machine learning predictions and statistical tests are incorporated in APRIL to predict novel disease-associated genes based on the expanded regulatory networks. Important features of non-coding regulatory elements and genetic variants are prioritized in network-based predictions, providing systems-level insights on the mechanisms of transcriptional dysregulation associated with complex diseases.

Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear.

METHODS

In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes.

RESULTS

We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein-protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP.

CONCLUSION

Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.

Free Wanderer Powder regulates AMPA receptor homeostasis in chronic restraint stress-induced rat model of depression with liver-depression and spleen-deficiency syndrome

Free Wanderer Powder (FWP) is a classic formula for depression with digestive dysfunctions, i.e., liver-depression and spleen-deficiency syndrome (LDSDS) in Chinese Medicine. But its protective mechanism has not been fully clarified. Here a chronic restraint stress (CRS) induced rat model showed depression with LDSDS in food intake, metabolism, and behaviour tests. Then 75 rats were randomly divided, and received CRS and different treatment with behaviour tests. Expressions of c-Fos and AMPA-type glutamate receptor subunits GluR1-3 in hippocampus CA1, CA3, DG and amygdala BLA were detected by immunohistochemistry, western blot and RT-PCR, respectively. In CRS rats, FWP alleviated depressive behaviour and c-Fos expression. FWP suppressed the increasement of GluR1 in CA1 and DG, p-GluR1 in CA1, and p-GluR2 and GluR3 in BLA. FWP also blocked the decrease of GluR1 and Glur2/3 in CA3, p-GluR1 in CA3, and p-GluR2 in CA1 and CA3. Furthermore, constituents of FWP and their potential targets were explored using UHPLC-MS and systematic bioinformatics analysis. There were 23 constituents identified in FWP, 9 of which regulated glutamatergic synapse. Together, these results suggest that FWP contains effective constituents and alleviates depression with LDSDS by regulating AMPA-type glutamate receptor homeostasis in amygdala and hippocampus.

Guanxinshutong Alleviates Atherosclerosis by Suppressing Oxidative Stress and Proinflammation in ApoE-/- Mice

Atherosclerosis (AS) is a chronic progressive disease related to dyslipidemia, inflammation, and oxidative stress. Guanxinshutong capsule (GXST), a traditional Chinese medicine, has been widely used in treating coronary atherosclerotic heart disease, while its mechanism actions on AS are still not to be well addressed. Our present study is aimed to examine the effect of GXST on AS and elucidate the multitarget mechanisms of GXST on AS. Network pharmacology analysis was employed to screen the multitarget mechanisms of GXST on AS. ApoE^−/− mice were used to validate these effects. Circulating lipid profile and oxidative stress-related factors were measured by the Elisa kit. Furthermore, the aortic trunk and aortic root were excised for oil red O staining, histopathological and immunohistochemical analysis. We first discovered that GXST was clued to exert synergistically antiatherosclerosis properties including lipid-lowering, anti-inflammation, and antioxidation through the computational prediction based on a network pharmacology simulation. Next, the validation experiments in atherosclerosis mice provided evidence that GXST significantly reduced atherosclerotic lesions, increased collagen deposition, and attenuated LV remodeling to some extent. Mechanistically, GXST modulated lipid profile, downregulated the level of inflammatory cytokines and NF-κBp65. GXST also reduced the activity of oxidative parameter MDA and upregulated the activities of antioxidant enzymes (SOD and GSH) compared with the AS model group. In conclusion, GXST intervention might attenuate atherosclerosis by mechanisms involving reducing lipid deposition, modulating oxidative stress and inflammatory responses, but a larger controlled trial is necessary for confirmation.

RepCOOL: computational drug repositioning via integrating heterogeneous biological networks

Background

It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time.

Methods

In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease.

Results

The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II.

Conclusion

Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.

A Network Pharmacological Approach to Investigate the Mechanism of Action of Active Ingredients of Epimedii Herba and Their Potential Targets in Treatment of Alzheimer's Disease

Background

Epimedii Herba is a traditional Chinese herbal medicine used to treat central nervous system diseases such as Alzheimer’s disease in China. However, the pharmacological mechanism is unclear. To investigate the mechanisms of Epimedii Herba in the treatment of Alzheimer’s disease, we assessed effective compounds, corresponding targets, and related pathways of Epimedii Herba in the treatment of Alzheimer’s disease based on network pharmacology.

Material/Methods

The active components and targets of Epimedii Herba were obtained through the TCMSP database and the DrugBank database. The DisGeNET database and GeneCards database were used to search for Alzheimer’s disease targets. The common targets of components and disease were obtained by Wayne diagram. Gene ontology (GO) analysis and enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed using the DAVID database. The component-target-pathway interaction network model was constructed using Cytoscape software. Auto Duck Vina software was used for molecular docking to analyze the affinity of the key ingredients and the main targets.

Results

We screened 17 active ingredients and 27 key targets of Epimedii Herba in the treatment of Alzheimer’s disease, which were related to the HIF-1 signaling pathway, TNF signaling pathway, PI3K-Akt signaling pathway, NF-κB signaling pathway, VEGF signaling pathway, and sphingolipid signaling pathway.

Conclusions

Based on network pharmacology, the multi-component, multi-target, and multi-pathway characteristics of Epimedii Herba in the treatment of Alzheimer’s disease were explored. Our results provide new ideas for future pharmacological and experimental research on Epimedii Herba in the treatment of Alzheimer’s disease.

Cilia interactome with predicted protein-protein interactions reveals connections to Alzheimer's disease, aging and other neuropsychiatric processes

Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein-protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia "interactome" consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer's disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.

A novel subnetwork representation learning method for uncovering disease-disease relationships

Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has been extensively studied by a large number of researchers, its prognosis is still poor. Since therapeutic advance can be obtained based on gene signatures, there is an urgent need to discover genes related to breast cancer that may help uncover the mechanisms in cancer progression. We propose a deep learning method for the discovery of breast cancer-related genes by using Capsule Network based Modeling of Multi-omics Data (CapsNetMMD). In CapsNetMMD, we make use of known breast cancer-related genes to transform the issue of gene identification into the issue of supervised classification. The features of genes are generated through comprehensive integration of multi-omics data, e.g., mRNA expression, z scores for mRNA expression, DNA methylation, and two forms of DNA copy-number alterations (CNAs). By modeling features based on the capsule network, we identify breast cancer-related genes with a significantly better performance than other existing machine learning methods. The predicted genes with prognostic values play potential important roles in breast cancer and may serve as candidates for biologists and medical scientists in the future studies of biomarkers.

Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

BACKGROUND

Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown.

METHODS

In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma.

RESULTS

Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group.

CONCLUSIONS

We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Network-based modeling of drug effects on disease module in systemic sclerosis

The network-based proximity between drug targets and disease genes can provide novel insights regarding the repercussions, interplay, and repositioning of drugs in the context of disease. Current understanding and treatment for reversing of the fibrotic process is limited in systemic sclerosis (SSc). We have developed a network-based analysis for drug effects that takes into account the human interactome network, proximity measures between drug targets and disease-associated genes, genome-wide gene expression and disease modules that emerge through pertinent analysis. Currently used and potential drugs showed a wide variation in proximity to SSc-associated genes and distinctive proximity to the SSc-relevant pathways, depending on their class and targets. Tyrosine kinase inhibitors (TyKIs) approach disease gene through multiple pathways, including both inflammatory and fibrosing processes. The SSc disease module includes the emerging molecular targets and is in better accord with the current knowledge of the pathophysiology of the disease. In the disease-module network, the greatest perturbing activity was shown by nintedanib, followed by imatinib, dasatinib, and acetylcysteine. Suppression of the SSc-relevant pathways and alleviation of the skin fibrosis was remarkable in the inflammatory subsets of the SSc patients receiving TyKI therapy. Our results show that network-based drug-disease proximity offers a novel perspective into a drug’s therapeutic effect in the SSc disease module. This could be applied to drug combinations or drug repositioning, and be helpful guiding clinical trial design and subgroup analysis.

Integrative biomarker detection on high-dimensional gene expression data sets: a survey on prior knowledge approaches

Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection.

Non-BRAF Mutant Melanoma: Molecular Features and Therapeutical Implications

Melanoma is one of the most aggressive tumors of the skin, and its incidence is growing worldwide. Historically considered a drug resistant disease, since 2011 the therapeutic landscape of melanoma has radically changed. Indeed, the improved knowledge of the immune system and its interactions with the tumor, and the ever more thorough molecular characterization of the disease, has allowed the development of immunotherapy on the one hand, and molecular target therapies on the other. The increased availability of more performing technologies like Next-Generation Sequencing (NGS), and the availability of increasingly large genetic panels, allows the identification of several potential therapeutic targets. In light of this, numerous clinical and preclinical trials are ongoing, to identify new molecular targets. Here, we review the landscape of mutated non-BRAF skin melanoma, in light of recent data deriving from Whole-Exome Sequencing (WES) or Whole-Genome Sequencing (WGS) studies on melanoma cohorts for which information on the mutation rate of each gene was available, for a total of 10 NGS studies and 992 samples, focusing on available, or in experimentation, targeted therapies beyond those targeting mutated BRAF. Namely, we describe 33 established and candidate driver genes altered with frequency greater than 1.5%, and the current status of targeted therapy for each gene. Only 1.1% of the samples showed no coding mutations, whereas 30% showed at least one mutation in the RAS genes (mostly NRAS) and 70% showed mutations outside of the RAS genes, suggesting potential new roads for targeted therapy. Ongoing clinical trials are available for 33.3% of the most frequently altered genes.

Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases

Protein kinases are essential for signal transduction and control of most cellular processes, including metabolism, membrane transport, motility, and cell cycle. Despite the critical role of kinases in cells and their strong association with diseases, good coverage of their interactions is available for only a fraction of the 535 human kinases. Here, we present a comprehensive mass-spectrometry-based analysis of a human kinase interaction network covering more than 300 kinases. The interaction dataset is a high-quality resource with more than 5,000 previously unreported interactions. We extensively characterized the obtained network and were able to identify previously described, as well as predict new, kinase functional associations, including those of the less well-studied kinases PIM3 and protein O-mannose kinase (POMK). Importantly, the presented interaction map is a valuable resource for assisting biomedical studies. We uncover dozens of kinase-disease associations spanning from genetic disorders to complex diseases, including cancer.

Endocrine disrupting chemicals and COVID-19 relationships: a computational systems biology approach

Background

Patients at high risk of severe forms of COVID-19 frequently suffer from chronic diseases, but other risk factors may also play a role. Environmental stressors, such as endocrine disrupting chemicals (EDCs), can contribute to certain chronic diseases and might aggravate the course of COVID-19.

Objectives

To explore putative links between EDCs and COVID-19 severity, an integrative systems biology approach was constructed and applied.

Methods

As a first step, relevant data sets were compiled from major data sources. Biological associations of major EDCs to proteins were extracted from the CompTox database. Associations between proteins and diseases known as important COVID-19 comorbidities were obtained from the GeneCards and DisGeNET databases. Based on these data, we developed a tripartite network (EDCs-proteins-diseases) and used it to identify proteins overlapping between the EDCs and the diseases. Signaling pathways for common proteins were then investigated by over-representation analysis.

Results

We found several statistically significant pathways that may be dysregulated by EDCs and that may also be involved in COVID-19 severity. The Th17 and the AGE/RAGE signaling pathways were particularly promising.

Conclusions

Pathways were identified as possible targets of EDCs and as contributors to COVID-19 severity, thereby highlighting possible links between exposure to environmental chemicals and disease development. This study also documents the application of computational systems biology methods as a relevant approach to increase the understanding of molecular mechanisms linking EDCs and human diseases, thereby contributing to toxicology prediction.

Extracellular proteostasis prevents aggregation during pathogenic attack

In metazoans, the secreted proteome participates in intercellular signalling and innate immunity, and builds the extracellular matrix scaffold around cells. Compared with the relatively constant intracellular environment, conditions for proteins in the extracellular space are harsher, and low concentrations of ATP prevent the activity of intracellular components of the protein quality-control machinery. Until now, only a few bona fide extracellular chaperones and proteases have been shown to limit the aggregation of extracellular proteins^1-5. Here we performed a systematic analysis of the extracellular proteostasis network in Caenorhabditis elegans with an RNA interference screen that targets genes that encode the secreted proteome. We discovered 57 regulators of extracellular protein aggregation, including several proteins related to innate immunity. Because intracellular proteostasis is upregulated in response to pathogens^6-9, we investigated whether pathogens also stimulate extracellular proteostasis. Using a pore-forming toxin to mimic a pathogenic attack, we found that C. elegans responded by increasing the expression of components of extracellular proteostasis and by limiting aggregation of extracellular proteins. The activation of extracellular proteostasis was dependent on stress-activated MAP kinase signalling. Notably, the overexpression of components of extracellular proteostasis delayed ageing and rendered worms resistant to intoxication. We propose that enhanced extracellular proteostasis contributes to systemic host defence by maintaining a functional secreted proteome and avoiding proteotoxicity.

Investigation on the Mechanism of Qubi Formula in Treating Psoriasis Based on Network Pharmacology

Objective

To elucidate the pharmacological mechanisms of Qubi Formula (QBF), a traditional Chinese medicine (TCM) formula which has been demonstrated as an effective therapy for psoriasis in China.

Methods

The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, BATMAN-TCM database, and literature search were used to excavate the pharmacologically active ingredients of QBF and to predict the potential targets. Psoriasis-related targets were obtained from Therapeutic Target Database (TTD), DrugBank database (DBD), MalaCards database, and DisGeNET database. Then, we established the network concerning the interactions of potential targets of QBF with well-known psoriasis-related targets by using protein-protein interaction (PPI) data in String database. Afterwards, topological parameters (including DNMC, Degree, Closeness, and Betweenness) were calculated to excavate the core targets of Qubi Formula in treating psoriasis (main targets in the PPI network). Cytoscape was used to construct the ingredients-targets core network for Qubi Formula in treating psoriasis, and ClueGO was used to perform GO-BP and KEGG pathway enrichment analysis on these core targets.

Results

The ingredient-target-disease core network of QBF in treating psoriasis was screened to contain 175 active ingredients, which corresponded to 27 core targets. Additionally, enrichment analysis suggested that targets of QBF in treating psoriasis were mainly clustered into multiple biological processes (associated with nuclear translocation of proteins, cellular response to multiple stimuli (immunoinflammatory factors, oxidative stress, and nutrient substance), lymphocyte activation, regulation of cyclase activity, cell-cell adhesion, and cell death) and related pathways (VEGF, JAK-STAT, TLRs, NF-κB, and lymphocyte differentiation-related pathways), indicating the underlying mechanisms of QBF on psoriasis.

Conclusion

In this work, we have successfully illuminated that Qubi Formula could relieve a wide variety of pathological factors (such as inflammatory infiltration and abnormal angiogenesis) of psoriasis in a "multicompound, multitarget, and multipathway" manner by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of QBF on psoriasis treatment.

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1–2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.

A bioinformatics investigation into the pharmacological mechanisms of javanica oil emulsion injection in non-small cell lung cancer based on network pharmacology methodologies

Background

Javanica oil emulsion injection (JOEI) is an effective therapeutic option for patients with non-small cell lung cancer (NSCLC), but its mechanisms have not been fully elucidated.

Methods

In this study, we utilized network pharmacology to systematically investigate the bioactive components and targets of JOEI, identify common targets in NSCLC, and understand and evaluate the underlying mechanism of JOEI in the treatment of NSCLC through expression level, correlation, enrichment, Cox, survival and molecular docking analyses. The results indicated that five compounds of JOEI interact with five pivotal targets (LDLR, FABP4, ABCB1, PTGS2, and SDC4) that might be strongly correlated with the JOEI-mediated treatment of NSCLC.

Results

The expression level analysis demonstrated that NSCLC tissues exhibit low expression of FABP4, ABCB1, LDLR and PTGS2 and high SDC4 expression. According to the correlation analysis, a decrease in FABP4 expression was strongly correlated with decreases in LDLR and ABCB1, and a decrease in LDLR was strongly correlated with decreased PTGS2 and increased in SDC4 expression. Cox and survival analyses showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group (p = 0.00388). In the survival analysis, the area under the curve (AUC) showed that the pivotal gene model exhibited the best predictive capacity over 4 years (AUC = 0.613). Moreover, the molecular docking analysis indicated that LDLR, FABP4, ABCB1, PTGS2 and SDC4 exhibit good binding activity with the corresponding compounds.

Conclusion

In conclusion, this study predicted and verified that the mechanism of JOEI against NSCLC involves multiple targets and signaling pathways. Furthermore, this study provides candidate targets for the treatment of NSCLC, lays a good foundation for further experimental research and promotes the reasonable application of JOEI in clinical treatment.

Supervised learning is an accurate method for network-based gene classification

BACKGROUND

Assigning every human gene to specific functions, diseases and traits is a grand challenge in modern genetics. Key to addressing this challenge are computational methods, such as supervised learning and label propagation, that can leverage molecular interaction networks to predict gene attributes. In spite of being a popular machine-learning technique across fields, supervised learning has been applied only in a few network-based studies for predicting pathway-, phenotype- or disease-associated genes. It is unknown how supervised learning broadly performs across different networks and diverse gene classification tasks, and how it compares to label propagation, the widely benchmarked canonical approach for this problem.

RESULTS

In this study, we present a comprehensive benchmarking of supervised learning for network-based gene classification, evaluating this approach and a classic label propagation technique on hundreds of diverse prediction tasks and multiple networks using stringent evaluation schemes. We demonstrate that supervised learning on a gene's full network connectivity outperforms label propagaton and achieves high prediction accuracy by efficiently capturing local network properties, rivaling label propagation's appeal for naturally using network topology. We further show that supervised learning on the full network is also superior to learning on node embeddings (derived using node2vec), an increasingly popular approach for concisely representing network connectivity. These results show that supervised learning is an accurate approach for prioritizing genes associated with diverse functions, diseases and traits and should be considered a staple of network-based gene classification workflows.

AVAILABILITY AND IMPLEMENTATION

The datasets and the code used to reproduce the results and add new gene classification methods have been made freely available.

CONTACT

arjun@msu.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deficiency in IκBα in the intestinal epithelium leads to spontaneous inflammation and mediates apoptosis in the gut

The IκB kinase (IKK)-NF-κB signaling pathway plays a multifaceted role in inflammatory bowel disease (IBD): on the one hand, it protects from apoptosis; on the other, it activates transcription of numerous inflammatory cytokines and chemokines. Although several murine models of IBD rely on disruption of IKK-NF-κB signaling, these involve either knockouts of a single family member of NF-κB or of upstream kinases that are known to have additional, NF-κB-independent, functions. This has made the distinct contribution of NF-κB to homeostasis in intestinal epithelium cells difficult to assess. To examine the role of constitutive NF-κB activation in intestinal epithelial cells, we generated a mouse model with a tissue-specific knockout of the direct inhibitor of NF-κB, Nfkbia/IκBα. We demonstrate that constitutive activation of NF-κB in intestinal epithelial cells induces several hallmarks of IBD including increased apoptosis, mucosal inflammation in both the small intestine and the colon, crypt hyperplasia, and depletion of Paneth cells, concomitant with aberrant Wnt signaling. To determine which NF-κB-driven phenotypes are cell-intrinsic, and which are extrinsic and thus require the immune compartment, we established a long-term organoid culture. Constitutive NF-κB promoted stem-cell proliferation, mis-localization of Paneth cells, and sensitization of intestinal epithelial cells to apoptosis in a cell-intrinsic manner. Increased number of stem cells was accompanied by a net increase in Wnt activity in organoids. Because aberrant Wnt signaling is associated with increased risk of cancer in IBD patients and because NFKBIA has recently emerged as a risk locus for IBD, our findings have critical implications for the clinic. In a context of constitutive NF-κB, our findings imply that general anti-inflammatory or immunosuppressive therapies should be supplemented with direct targeting of NF-κB within the epithelial compartment in order to attenuate apoptosis, inflammation, and hyperproliferation. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Establishment of a CALU, AURKA, and MCM2 gene panel for discrimination of metastasis from primary colon and lung cancers

Metastasis is known as a key step in cancer recurrence and could be stimulated by multiple factors. Calumenin (CALU) is one of these factors which has a direct impact on cancer metastasis and yet, its underlined mechanisms have not been completely elucidated. The current study was aimed to identify CALU co-expressed genes, their signaling pathways, and expression status within the human cancers. To this point, CALU associated genes were visualized using the Cytoscape plugin BisoGenet and annotated with the Enrichr web-based application. The list of CALU related diseases was retrieved using the DisGenNet, and cancer datasets were downloaded from The Cancer Genome Atlas (TCGA) and analyzed with the Cufflink software. ROC curve analysis was used to estimate the diagnostic accuracy of DEGs in each cancer, and the Kaplan-Meier survival analysis was performed to plot the overall survival of patients. The protein level of the signature biomarkers was measured in 40 biopsy specimens and matched adjacent normal tissues collected from CRC and lung cancer patients. Analysis of CALU co-expressed genes network in TCGA datasets indicated that the network is markedly altered in human colon (COAD) and lung (LUAD) cancers. Diagnostic accuracy estimation of differentially expressed genes showed that a gene panel consisted of CALU, AURKA, and MCM2 was able to successfully distinguish cancer tumors from healthy samples. Cancer cases with abnormal expression of the signature genes had a significantly lower survival rate than other patients. Additionally, comparison of CALU, AURKA, and MCM2 proteins between healthy samples, early and advanced tumors showed that the level of these proteins was increased through normal-carcinoma transition in both types of cancers. These data indicate that the interactions between CALU, AURKA, and MCM2 has a pivotal role in cancer development, and thereby needs to be explored in the future.

The DisGeNET knowledge platform for disease genomics: 2019 update

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

Open Targets Platform: new developments and updates two years on

The Open Targets Platform integrates evidence from genetics, genomics, transcriptomics, drugs, animal models and scientific literature to score and rank target-disease associations for drug target identification. The associations are displayed in an intuitive user interface (https://www.targetvalidation.org), and are available through a REST-API (https://api.opentargets.io/v3/platform/docs/swagger-ui) and a bulk download (https://www.targetvalidation.org/downloads/data). In addition to target-disease associations, we also aggregate and display data at the target and disease levels to aid target prioritisation. Since our first publication two years ago, we have made eight releases, added new data sources for target-disease associations, started including causal genetic variants from non genome-wide targeted arrays, added new target and disease annotations, launched new visualisations and improved existing ones and released a new web tool for batch search of up to 200 targets. We have a new URL for the Open Targets Platform REST-API, new REST endpoints and also removed the need for authorisation for API fair use. Here, we present the latest developments of the Open Targets Platform, expanding the evidence and target-disease associations with new and improved data sources, refining data quality, enhancing website usability, and increasing our user base with our training workshops, user support, social media and bioinformatics forum engagement.

An Analysis of the Anti-Neuropathic Effects of Qi She Pill Based on Network Pharmacology

Background

Qi She Pill (QSP) is a traditional prescription for the treatment of neuropathic pain (NP) that is widely used in China. However, no network pharmacology studies of QSP in the treatment of NP have been conducted to date.

Objective

To verify the potential pharmacological effects of QSP on NP, its components were analyzed via target docking and network analysis, and network pharmacology methods were used to study the interactions of its components.

Materials and Methods

Information on pharmaceutically active compounds in QSP and gene information related to NP were obtained from public databases, and a compound-target network and protein-protein interaction network were constructed to study the mechanism of action of QSP in the treatment of NP. The mechanism of action of QSP in the treatment of NP was analyzed via Gene Ontology (GO) biological process annotation and Kyoto Gene and Genomics Encyclopedia (KEGG) pathway enrichment, and the drug-like component-target-pathway network was constructed.

Results

The compound-target network contained 60 compounds and 444 corresponding targets. The key active compounds included quercetin and beta-sitosterol. Key targets included PTGS2 and PTGS1. The protein-protein interaction network of the active ingredients of QSP in the treatment of NP featured 48 proteins, including DRD2, CHRM, β2-adrenergic receptor, HTR2A, and calcitonin gene-related peptide. In total, 53 GO entries, including 35 biological process items, 7 molecular function items, and 11 cell related items, were identified. In addition, eight relevant (KEGG) pathways were identified, including calcium, neuroactive ligand-receptor interaction, and cAMP signaling pathways.

Conclusion

Network pharmacology can help clarify the role and mechanism of QSP in the treatment of NP and provide a foundation for further research.

Post-translational Modification Crosstalk and Hotspots in Sirtuin Interactors Implicated in Cardiovascular Diseases

Sirtuins are protein deacetylases that play a protective role in cardiovascular diseases (CVDs), as well as many other diseases. Absence of sirtuins can lead to hyperacetylation of both nuclear and mitochondrial proteins leading to metabolic dysregulation. The protein post-translational modifications (PTMs) are known to crosstalk among each other to bring about complex phenotypic outcomes. Various PTM types such as acetylation, ubiquitination, and phosphorylation, and so on, drive transcriptional regulation and metabolism, but such crosstalks are poorly understood. We integrated protein–protein interactions (PPI) and PTMs from several databases to integrate information on 1,251 sirtuin-interacting proteins, of which 544 are associated with cardiac diseases. Based on the ∼100,000 PTM sites obtained for sirtuin interactors, we observed that the frequency of PTM sites (83 per protein), as well as PTM types (five per protein), is higher than the global average for human proteome. We found that ∼60–70% PTM sites fall into ordered regions. Approximately 83% of the sirtuin interactors contained at least one competitive crosstalk (in situ) site, with half of the sites occurring in CVD-associated proteins. A large proportion of identified crosstalk sites were observed for acetylation and ubiquitination competition. We identified 614 proteins containing PTM hotspots (≥5 PTM sites) and 133 proteins containing crosstalk hotspots (≥3 crosstalk sites). We observed that a large proportion of disease-associated sequence variants were found in PTM motifs of CVD proteins. We identified seven proteins (TP53, LMNA, MAPT, ATP2A2, NCL, APEX1, and HIST1H3A) containing disease-associated variants in PTM and crosstalk hotspots. This is the first comprehensive bioinformatics analysis on sirtuin interactors with respect to PTMs and their crosstalks. This study forms a platform for generating interesting hypotheses that can be tested for a deeper mechanistic understanding gained or derived from big-data analytics.

Ephrin-B1 Is a Novel Biomarker of Bladder Cancer Aggressiveness. Studies in Murine Models and in Human Samples

Bladder cancer (BC) is the ninth most common cancer worldwide, but molecular changes are still under study. During tumor progression, Epithelial cadherin (E-cadherin) expression is altered and β-catenin may be translocated to the nucleus, where it acts as co-transcription factor of tumor invasion associated genes. This investigation further characterizes E-cadherin and β-catenin associated changes in BC, by combining bioinformatics, an experimental murine cell model (MB49/MB49-I) and human BC samples. In in silico studies, a DisGeNET (gene-disease associations database) analysis identified CDH1 (E-cadherin gene) as one with highest score among 130 BC related-genes. COSMIC mutation analysis revealed CDH1 low mutations rates. Compared to MB49 control BC cells, MB49-I invasive cells showed decreased E-cadherin expression, E- to P-cadherin switch, higher β-catenin nuclear signal and lower cytoplasmic p-Ser33-β-catenin signal, higher Ephrin-B1 ligand and EphB2 receptor expression, higher Phospho-Stat3 and Urokinase-type Plasminogen Activator (UPA), and UPA receptor expression. MB49-I cells transfected with Ephrin-B1 siRNA showed lower migratory and invasive capacity than control cells (scramble siRNA). By immunohistochemistry, orthotopic MB49-I tumors had lower E-cadherin, increased nuclear β-catenin, lower pSer33-β-catenin cytoplasmic signal, and higher Ephrin-B1 expression than MB49 tumors. Similar changes were found in human BC tumors, and 83% of infiltrating tumors depicted a high Ephrin-B1 stain. An association between higher Ephrin-B1 expression and higher stage and tumor grade was found. No association was found between abnormal E-cadherin signal, Ephrin-B1 expression or clinical-pathological parameter. This study thoroughly analyzed E-cadherin and associated changes in BC, and reports Ephrin-B1 as a new marker of tumor aggressiveness.

Investigation of cardiovascular protective effect of Shenmai injection by network pharmacology and pharmacological evaluation

BACKGROUND

Shenmai injection (SMI) has been used in the treatment of cardiovascular disease (CVD), such as heart failure, myocardial ischemia and coronary heart disease. It has been found to have efficacy on doxorubicin (DOX)-induced cardiomyopathy. The aims of this study were to explore the underlying molecular mechanisms of SMI treatment on CVD by using network pharmacology and its protective effect on DOX-induced cardiotoxicity by in vitro and in vivo experiment based on network pharmacology prediction.

METHODS

Network pharmacology method was used to reveal the relationship between ingredient-target-disease and function-pathway of SMI on the treatment of CVD. Chemical ingredients of SMI were collected form TCMSP, BATMAN-TCM and HIT Database. Drugbank, DisGeNET and OMIM Database were used to obtain potential targets for CVD. Networks were visualized utilizing Cytoscape software, and the enrichment analysis was performed using IPA system. Finally, cardioprotective effects and predictive mechanism confirmation of SMI were investigated in H9c2 rat cardiomyocytes and DOX-injured C57BL/6 mice.

RESULTS

An ingredient-target-disease & function-pathway network demonstrated that 28 ingredients derived from SMI modulated 132 common targets shared by SMI and CVD. The analysis of diseases & functions, top pathways and upstream regulators indicated that the cardioprotective effects of SMI might be associated with 28 potential ingredients, which regulated the 132 targets in cardiovascular disease through regulation of G protein-coupled receptor signaling. In DOX-injured H9c2 cardiomyocytes, SMI increased cardiomyocytes viability, prevented cell apoptosis and increased PI3K and p-Akt expression. This protective effect was markedly weakened by PI3K inhibitor LY294002. In DOX-treated mice, SMI treatment improved cardiac function, including enhancement of ejection fraction and fractional shortening.

CONCLUSIONS

Collectively, the protective effects of SMI on DOX-induced cardiotoxicity are possibly related to the activation of the PI3K/Akt pathway, as the downstream of G protein-coupled receptor signaling pathway.

Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism

Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.'s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.

CLING: Candidate Cancer-Related lncRNA Prioritization via Integrating Multiple Biological Networks

Identification and characterization of lncRNAs in cancer with a view to their application in improving diagnosis and therapy remains a major challenge that requires new and innovative approaches. We have developed an integrative framework termed “CLING”, aimed to prioritize candidate cancer-related lncRNAs based on their associations with known cancer lncRNAs. CLING focuses on joint optimization and prioritization of all candidates for each cancer type by integrating lncRNA topological properties and multiple lncRNA-centric networks. Validation analyses revealed that CLING is more effective than prioritization based on a single lncRNA network. Reliable AUC (Area Under Curve) scores were obtained across 10 cancer types, ranging from 0.85 to 0.94. Several novel lncRNAs predicted in the top 10 candidates for various cancer types have been confirmed by recent biological experiments. Furthermore, using a case study on liver hepatocellular carcinoma as an example, CLING facilitated the successful identification of novel cancer lncRNAs overlooked by differential expression analyses (DEA). This time- and cost-effective computational model may provide a valuable complement to experimental studies and assist in future investigations on lncRNA involvement in the pathogenesis of cancers. We have developed a web-based server for users to rapidly implement CLING and visualize data, which is freely accessible at http://bio-bigdata.hrbmu.edu.cn/cling/. CLING has been successfully applied to predict a few potential lncRNAs from thousands of candidates for many cancer types.

Severe hydroxymethylbilane synthase deficiency causes depression-like behavior and mitochondrial dysfunction in a mouse model of homozygous dominant acute intermittent porphyria

Acute intermittent porphyria (AIP) is an autosomal dominant inborn error of heme biosynthesis due to a pathogenic mutation in the Hmbs gene, resulting in half-normal activity of hydroxymethylbilane synthase. Factors that induce hepatic heme biosynthesis induce episodic attacks in heterozygous patients. The clinical presentation of acute attacks involves the signature neurovisceral pain and may include psychiatric symptoms. Here we used a knock-in mouse line that is biallelic for the Hmbs c.500G > A (p.R167Q) mutation with ~ 5% of normal hydroxymethylbilane synthase activity to unravel the consequences of severe HMBS deficiency on affective behavior and brain physiology. Hmbs knock-in mice (KI mice) model the rare homozygous dominant form of AIP and were used as tool to elucidate the hitherto unknown pathophysiology of the behavioral manifestations of the disease and its neural underpinnings. Extensive behavioral analyses revealed a selective depression-like phenotype in Hmbs KI mice; transcriptomic and immunohistochemical analyses demonstrated aberrant myelination. The uncovered compromised mitochondrial function in the hippocampus of knock-in mice and its ensuing neurogenic and neuroplastic deficits lead us to propose a mechanistic role for disrupted mitochondrial energy production in the pathogenesis of the behavioral consequences of severe HMBS deficiency and its neuropathological sequelae in the brain.

An Integrated Pharmacology-Based Analysis for Antidepressant Mechanism of Chinese Herbal Formula Xiao-Yao-San

Clinical studies and basic science experiments have widely demonstrated the antidepressant and anxiolytic effects of the herbal formula Xiao-Yao-San (XYS). However, the system mechanism of these effects has not been fully characterized. The present study conducted a comprehensive network pharmacological analysis of XYS and sorted all pharmacologically active components (149) through the TCMSP webserver. Then, all potential molecular targets (449) were predicted, of which there were 99 genes clearly related to depression. To further investigate the mechanism of antidepressant effects of XYS, a compound-depression targets (C-DTs) network was constructed, and Gene Ontology (GO) functional and KEGG pathway enrichment analyses were performed for the 99 targets. Enrichment results revealed that XYS could regulate multiple aspects of depression through these targets, related to metabolism, neuroendocrine function, and neuroimmunity. Prediction and analysis of protein–protein interactions resulted in selection of three hub genes (AKT1, TP53, and VEGFA). In addition, a total of seven ingredients from XYS could act on these hub genes and they were identified through ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS), including paeoniflorin, quercetin, luteolin, acacetin, aloe-emodin, Glyasperin C, kaempferol. Hereafter, we investigated the effects of paeoniflorin and its predicted target, the results suggest that it can reverse the neurotoxicity produced by CORT and could be a neuroprotective effect by promoting the phosphorylation of Akt. Overall, our research revealed the complicated antidepressant mechanism of XYS, and also provided a rational strategy for revealing the complex composition and function of Chinese herbal formula.

Network Pharmacology Identifies the Mechanisms of Action of TaohongSiwu Decoction Against Essential Hypertension

BACKGROUND TaohongSiwu decoction (THSWT), a traditional herbal formula, has been used to treat cardiovascular and cerebrovascular diseases such as essential hypertension (EH) in China. However, the pharmacological mechanism is not clear. To investigate the mechanisms of THSWT in the treatment of EH, we performed compounds, targets prediction and network analysis using a network pharmacology method. MATERIAL AND METHODS We selected chemical constituents and targets of THSWT according to TCMSP and UniProtKB databases and collected therapeutic targets on EH from Online Mendelian Inheritance in Man (OMIM), Drugbank and DisGeNET databases. The protein-protein interaction (PPI) was analyzed by using String database. Then network was constructed by using Cytoscape_v3.7.1, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software. RESULTS The results of our network pharmacology research showed that the THSWT, composed of 6 Chinese herbs, contained 15 compounds, and 23 genes regulated the main signaling pathways related to EH. Moreover, the PPI network based on targets of THSWT on EH revealed the interaction relationship between targets. These core compounds were 6 of the 15 disease-related compounds in the network, kaempferol, quercetin, luteolin, Myricanone, beta-sitosterol, baicalein, and the core genes contained ADRB2, CALM1, HMOX1, JUN, PPARG, and VEGFA, which were regulated by more than 3 compounds and significantly associated with Calcium signaling pathway, cGMP-PKG signaling pathway, cAMP signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and Ras signaling pathway. CONCLUSIONS This network pharmacological study can reveal potential mechanisms of multi-target and multi-component THSWT in the treatment of EH, provide a scientific basis for studying the mechanism.

Exploring the cross-phenotype network region of disease modules reveals concordant and discordant pathways between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

Chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are two pathologically distinct chronic lung diseases that are associated with cigarette smoking. Genetic studies have identified shared loci for COPD and IPF, including several loci with opposite directions of effect. The existence of additional shared genetic loci, as well as potential shared pathobiological mechanisms between the two diseases at the molecular level, remains to be explored. Taking a network-based approach, we built disease modules for COPD and IPF using genome-wide association studies-implicated genes. The two disease modules displayed strong disease signals in an independent gene expression data set of COPD and IPF lung tissue and showed statistically significant overlap and network proximity, sharing 19 genes, including ARHGAP12 and BCHE. To uncover pathways at the intersection of COPD and IPF, we developed a metric, NetPathScore, which prioritizes the pathways of a disease by their network overlap with another disease. Applying NetPathScore to the COPD and IPF disease modules enabled the determination of concordant and discordant pathways between these diseases. Concordant pathways between COPD and IPF included extracellular matrix remodeling, Mitogen-activated protein kinase (MAPK) signaling and ALK pathways, whereas discordant pathways included advanced glycosylation end product receptor signaling and telomere maintenance and extension pathways. Overall, our findings reveal shared molecular interaction regions between COPD and IPF and shed light on the congruent and incongruent biological processes lying at the intersection of these two complex diseases.

Development and validation of a lipogenic genes panel for diagnosis and recurrence of colorectal cancer

Background & aim

Accumulated evidence indicates that the elevation of lipid metabolism is an essential step in colorectal cancer (CRC) development, and analysis of the key lipogenic mediators may lead to identifying the new clinically useful prognostic gene signatures.

Methods

The expression pattern of 61 lipogenic genes was assessed between CRC tumors and matched adjacent normal tissues in a training set (n = 257) with the Mann-Whitney U test. Cox's proportional hazards model and the Kaplan–Meier method were used to identifying a lipogenic-biomarkers signature associated with the prognosis of CRC. The biomarkers signature was then confirmed in two independent validation groups, including a set of 223 CRC samples and an additional set of 203 COAD profiles retrieving from the Cancer Genome Atlas (TCGA).

Results

Five genes, including ACOT8, ACSL5, FASN, HMGCS2, and SCD1, were significantly enhanced in CRC tumors. Using the cutoff value 0.493, the samples were classified into high risk and low risk. The AUC of panel for discriminating of all, early (I-II stages), and advanced CRC (III-IV stages) were 0.8922, 0.8446, and 0.9162 (Training set), along with 0.8800, 0.8205, and 0.7351 (validation set I), and 0.9071, 0.8946, and 0.9107 (Validation set II), respectively. There was a reverse correlation between the high predicted point of panel and worse OS of CRC patients in training set (HR (95% CI): 0.1096 (0.07089–0.1694), P < 0.001), validation set I (HR (95% CI): 0.3350 (0.2116–0.5304), P < 0.001), and validation set II (HR (95% CI): 0.1568 (0.1090–0.2257), P < 0.001).

Conclusion

Our study showed that the panel of ACOT8/ACSL5/FASN/HMGBCS2/SCD1 genes had a better prognostic performance than validated clinical risk scales and is applicable for early detection of CRC and tumor recurrence.

Avian Binocularity and Adaptation to Nocturnal Environments: Genomic Insights from a Highly Derived Visual Phenotype

Typical avian eyes are phenotypically engineered for photopic vision (daylight). In contrast, the highly derived eyes of the barn owl (Tyto alba) are adapted for scotopic vision (dim light). The dramatic modifications distinguishing barn owl eyes from other birds include: 1) shifts in frontal orientation to improve binocularity, 2) rod-dominated retina, and 3) enlarged corneas and lenses. Some of these features parallel mammalian eye patterns, which are hypothesized to have initially evolved in nocturnal environments. Here, we used an integrative approach combining phylogenomics and functional phenotypes of 211 eye-development genes across 48 avian genomes representing most avian orders, including the stem lineage of the scotopic-adapted barn owl. Overall, we identified 25 eye-development genes that coevolved under intensified or relaxed selection in the retina, lens, cornea, and optic nerves of the barn owl. The agtpbp1 gene, which is associated with the survival of photoreceptor populations, was pseudogenized in the barn owl genome. Our results further revealed that barn owl retinal genes responsible for the maintenance, proliferation, and differentiation of photoreceptors experienced an evolutionary relaxation. Signatures of relaxed selection were also observed in the lens and cornea morphology-associated genes, suggesting that adaptive evolution in these structures was essentially structural. Four eye-development genes (ephb1, phactr4, prph2, and rs1) evolved in positive association with the orbit convergence in birds and under relaxed selection in the barn owl lineage, likely contributing to an increased reliance on binocular vision in the barn owl. Moreover, we found evidence of coevolutionary interactions among genes that are expressed in the retina, lens, and optic nerve, suggesting synergetic adaptive events. Our study disentangles the genomic changes governing the binocularity and low-light perception adaptations of barn owls to nocturnal environments while revealing the molecular mechanisms contributing to the shift from the typical avian photopic vision to the more-novel scotopic-adapted eye.

Associating 197 Chinese herbal medicine with drug targets and diseases using the similarity ensemble approach

Chinese herbal medicine (CHM) addresses complex diseases through polypharmacological interactions. However, systematic studies of herbal medicine pharmacology remain challenging due to the complexity of CHM ingredients and their interactions with various targets. In this study, we aim to address this challenge with computational approaches. We investigated the herb-target-disease associations of 197 commonly prescribed CHMs using the similarity ensemble approach and DisGeNET database. We demonstrated that this method can be applied to associate herbs with their putative targets. In the case study of three well-known herbs, Radix Glycyrrhizae, Flos Lonicerae, and Rhizoma Coptidis, approximately 70% of the predicted targets were supported by scientific literature. By linking 406 targets to 2439 annotated diseases, we further analyzed the pharmacological functions of 197 herbs. Finally, we proposed a strategy of target-oriented herbal formula design and illustrated the target profiles for four common chronic diseases, namely, Alzheimer's disease, depressive disorder, hypertensive disease, and non-insulin-dependent diabetes mellitus. This computational approach holds great potential in the target identification of herbs, understanding the molecular mechanisms of CHM, and designing novel herbal formulas.

The influence of rare variants in circulating metabolic biomarkers

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.