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

A Quantitative Tissue-Specific Landscape of Protein Redox Regulation during Aging

Mammalian tissues engage in specialized physiology that is regulated through reversible modification of protein cysteine residues by reactive oxygen species (ROS). ROS regulate a myriad of biological processes, but the protein targets of ROS modification that drive tissue-specific physiology in vivo are largely unknown. Here, we develop Oximouse, a comprehensive and quantitative mapping of the mouse cysteine redox proteome in vivo. We use Oximouse to establish several paradigms of physiological redox signaling. We define and validate cysteine redox networks within each tissue that are tissue selective and underlie tissue-specific biology. We describe a common mechanism for encoding cysteine redox sensitivity by electrostatic gating. Moreover, we comprehensively identify redox-modified disease networks that remodel in aged mice, establishing a systemic molecular basis for the long-standing proposed links between redox dysregulation and tissue aging. We provide the Oximouse compendium as a framework for understanding mechanisms of redox regulation in physiology and aging.

Exploring the mechanism of action Xianlingubao Prescription in the treatment of osteoporosis by network pharmacology

In this study, the network pharmacology analysis method was used to explore the bioactive components and targets of Xianlinggubao (XLGB) and further elucidate its potential biological mechanisms of action in the treatment of osteoporosis (OP). The bioactive compounds and predictive targets of XLGB were collected from the traditional Chinese medicine systems pharmacology databases and analysis platform(TCMSP), the Encyclopeida of traditional Chinese medicine (ETCM), traditional Chinese medicine Databse@Taiwan, ChEMBL, STITCH, and SymMap database. The targets corresponding to OP were obtained by using Online Mendelian Inheritance in Man® (OMIM), GeneCards, the National Center for Biotechnology Information-Gene database. The XLGB-OP targets were obtained by intersecting with the targets of XLGB and OP. Protien-Protien interaciton (PPI) network was constructed using STRING online database and analyzed using Cytoscape 3.7.0 software to screen out hub genes. Gene ontology (GO) and KEGG enrichment analysis of the target in the PPI network was conducted using the ClusterProfiler package in R with adjusted p-value<0.05. A total of 65 XLGB bioactive compounds were screened corresponding to 776 XLGB targets and 2556 OP targets. The GO analysis and KEGG enrichment analyses suggested XLGB played a therapeutic roles in OP treatment via the interleukin-17 signaling pathway, hypoxia-inducible factor-1 signaling pathway, insulin resistance, Th-17 signaling pathway, etc. Five hub genes (AKT1, MAPK1, MAPK8, TP53, and STAT3) were screened using the degree algorithm, and molecular docking stimulation results showed that most bioactive compounds of XLGB had strong binding efficiency with hub genes. Overall, this study laid the foundation for further in vivo and in vitro experimental research and expanded the clinical applications of XLGB.

Prediction of modes of action of components of traditional medicinal preparations

Traditional medicine preparations are used to treat many ailments in multiple regions across the world. Despite their widespread use, the mode of action of these preparations and their constituents are not fully understood. Traditional methods of elucidating the modes of action of these natural products (NPs) can be expensive and time consuming e. g. biochemical methods, bioactivity guided fractionation, etc. In this review, we discuss some methods for the prediction of the modes of action of traditional medicine preparations, both in mixtures and as isolated NPs. These methods are useful to predict targets of NPs before they are experimentally validated. Case studies of the applications of these methods are also provided herein.

Discovery of inflammatory bowel disease-associated miRNAs using a novel bipartite clustering approach

BACKGROUND

Multidimensional data mining from an integrated environment of different data sources is frequently performed in computational system biology. The molecular mechanism from the analysis of a complex network of gene-miRNA can aid to diagnosis and treatment of associated diseases.

METHODS

In this work, we mainly focus on finding inflammatory bowel disease (IBD) associated microRNAs (miRNAs) by biclustering the miRNA-target interactions aided by known IBD risk genes and their associated miRNAs collected from several sources. We rank different miRNAs by attributing to the dataset size and connectivity of IBD associated genes in the miRNA regulatory modules from biclusters. We search the association of some top-ranking miRNAs to IBD related diseases. We also search the network of discovered miRNAs to different diseases and evaluate the similarity of those diseases to IBD.

RESULTS

According to different literature, our results show the significance of top-ranking miRNA to IBD or related diseases. The ratio analysis supports our ranking method where the top 20 miRNA has approximately tenfold attachment to IBD genes. From disease-associated miRNA network analysis we found that 71% of different diseases attached to those miRNAs show more than 0.75 similarity scores to IBD.

CONCLUSION

We successfully identify some miRNAs related to IBD where the scoring formula and disease-associated network analysis show the significance of our method. This method can be a promising approach for isolating miRNAs for similar types of diseases.

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Discovering the genes mediating the interactions between chronic respiratory diseases in the human interactome

The molecular and clinical features of a complex disease can be influenced by other diseases affecting the same individual. Understanding disease-disease interactions is therefore crucial for revealing shared molecular mechanisms among diseases and designing effective treatments. Here we introduce Flow Centrality (FC), a network-based approach to identify the genes mediating the interaction between two diseases in a protein-protein interaction network. We focus on asthma and COPD, two chronic respiratory diseases that have been long hypothesized to share common genetic determinants and mechanisms. We show that FC highlights potential mediator genes between the two diseases, and observe similar outcomes when applying FC to 66 additional pairs of related diseases. Further, we perform in vitro perturbation experiments on a widely replicated asthma gene, GSDMB, showing that FC identifies candidate mediators of the interactions between GSDMB and COPD-associated genes. Our results indicate that FC predicts promising gene candidates for further study of disease-disease interactions.

Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis

Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein-protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

Identification of Prognostic Biomarkers and Drugs Targeting Them in Colon Adenocarcinoma: A Bioinformatic Analysis

Objective: To identify prognostic biomarkers and drugs that target them in colon adenocarcinoma (COAD) based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. Methods: The TCGA dataset was used to identify the top 50 upregulated differentially expressed genes (DEGs), and Gene Expression Omnibus profiles were used for validation. Survival analyses were conducted with the TCGA dataset using the RTCGAToolbox package in the R software environment. Drugs targeting the candidate prognostic biomarkers were searched in the DrugBank and herbal databases. Results: Among the top 50 upregulated DEGs in patients with COAD in the TCGA dataset, the Wnt signaling pathway and cytokine-cytokine receptor interactions and pathways in cancer Kyoto Encyclopedia of Genes and Genomes pathway analysis were enriched in DEGs. Tissue development and regulation of cell proliferation were the main Gene Ontology biological processes associated with upregulated DEGs. MYC and KLK6 were overexpressed in tumors validated in the TCGA, GSE41328, and GSE113513 databases (all P < .001) and were significantly associated with overall survival in patients with COAD (P = .021 and P = .047). Nadroparin and benzamidine were identified as inhibitors of MYC and KLK6 in DrugBank, and 8 herbs targeting MYC, including Da Huang (Radix Rhei Et Rhizome), Hu Zhang (Polygoni Cuspidati Rhizoma Et Radix), Huang Lian (Coptidis Rhizoma), Ban Xia (Arum Ternatum Thunb), Tu Fu Ling (Smilacis Glabrae Rhixoma), Lei Gong Teng (Tripterygii Radix), Er Cha (Catechu), and Guang Zao (Choerospondiatis Fructus), were identified. Conclusion: MYC and KLK6 may serve as candidate prognostic predictors and therapeutic targets in patients with COAD.

Loss of phosphatase activity in PTEN (phosphatase and tensin homolog deleted on chromosome ten) results in endometrial carcinoma in humans: An in-silico study

The tumour suppressor gene, PTEN (Phosphatase and Tensin homolog deleted on chromosome Ten), can act as both protein phosphatase and lipid phosphatase, is known to play a vital role in Pi3k signalling pathway. In humans, it is located at 10q23. Loss of its phosphatase and catalytic activity is associated with various types of cancers. This study focuses on evolution, understanding the somatic missense mutation in a particular residue of PTEN and understanding the molecular mechanism that leads to endometrial carcinoma through molecular docking. Mutational analysis of H123 position indicates that the missense mutation at first position of the codon CAC by G or T, result in aspartic acid or tyrosine instead of histidine and can have negative effect on the function of PTEN. Alongside, structural analysis showed mutated PTEN has lower stability than the normal. Additionally, SNPs dataset for endometrial carcinoma suggests H123 as strongly mutated residue. The mutation in phosphatase domain of PTEN along with its effect and interaction with substrate TLA1352 were systematically studied through molecular docking. Molecular interaction study reveals that the optimal substrate binding site in PTEN is unable to interact with the substrate in the mutated condition. This observation drew attention on the impact of mutation on disease biology and enabled us to conduct follow-up studies to retrieve novel molecular targets, such as mutated protein domain and modified Asp and Tyr sites, to design effective therapies to either prevent endometrial carcinoma or impede its progression.

Radix Rehmanniae and Corni Fructus against Diabetic Nephropathy via AGE-RAGE Signaling Pathway

BACKGROUND AND AIMS

Radix Rehmanniae and Corni Fructus (RC) have been widely applied to treat diabetic nephropathy (DN) for centuries. But the mechanism of how RC plays the therapeutic role against DN is unclear as yet.

METHODS

The information about RC was obtained from a public database. The active compounds of RC were screened by oral bioavailability (OB) and drug-likeness (DL). Gene ontology (GO) analysis was performed to realize the key targets of RC, and an active compound-potential target network was created. The therapeutic effects of RC active compounds and their key signal pathways were preliminarily probed via network pharmacology analysis and animal experiments.

RESULTS

In this study, 29 active compounds from RC and 64 key targets related to DN were collected using the network pharmacology method. The pathway enrichment analysis showed that RC regulated advanced glycosylation end product (AGE-) RAGE and IL-17 signaling pathways to treat DN. The animal experiments revealed that RC significantly improved metabolic parameters, inflammation renal structure, and function to protect the kidney against DN.

CONCLUSIONS

The results revealed the relationship between multicomponents and multitargets of RC. The administratiom of RC might remit the DM-induced renal damage through the AGE-RAGE signaling pathway to improve metabolic parameters and protect renal structure and function.

The common pathophysiologic threads between Asian Indian diabetic's 'Thin Fat Phenotype' and partial lipodystrophy: the peripheral adipose tissue transcriptomic evidences

T2D is a complex disease with poorly understood mechanisms. In Asian Indians, it is associated with “thin fat” phenotype which resembles with partial lipodystrophy. We hypothesized that disturbed expression of lipodystrophy genes might play a role in T2D pathogenesis. Therefore, we attempted to establish a link between these two diseases by studying the overlap between the network of lipodystrophy genes and the differentially expressed genes (DEGs) in the peripheral subcutaneous adipose tissue of Asian Indians diabetics. We found that 16, out of 138 lipodystrophy genes were differentially regulated in diabetics and around 18% overlap between their network and the DEGs; the expression level of lipodystrophy genes showed an association with disease-related intermediate phenotypic traits among diabetics but not in the control group. We also attempted to individualize the diabetic patients based on ±2 fold altered expression of lipodystrophy genes as compared to their average expression in the control group. In conclusion, significant overlap exists between some of the lipodystrophy genes and their network with DEGs in the peripheral adipose tissue in diabetics. They possibly play a role in the pathogenesis of diabetes and individualization of diabetics is possible based on their altered expression in their peripheral adipose tissue.

Integrated querying and version control of context-specific biological networks

MOTIVATION

Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks.

RESULTS

We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases.

CONCLUSION

Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications.

AVAILABILITY AND IMPLEMENTATION

VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion.

CONTACT

tyler.cowman@case.edu.

Genome-wide significant regions in 43 Utah high-risk families implicate multiple genes involved in risk for completed suicide

Suicide is the 10th leading cause of death in the United States. Although environment has undeniable impact, evidence suggests that genetic factors play a significant role in completed suicide. We linked a resource of ~ 4500 DNA samples from completed suicides obtained from the Utah Medical Examiner to genealogical records and medical records data available on over eight million individuals. This linking has resulted in the identification of high-risk extended families (7-9 generations) with significant familial risk of completed suicide. Familial aggregation across distant relatives minimizes effects of shared environment, provides more genetically homogeneous risk groups, and magnifies genetic risks through familial repetition. We analyzed Illumina PsychArray genotypes from suicide cases in 43 high-risk families, identifying 30 distinct shared genomic segments with genome-wide evidence (p = 2.02E-07-1.30E-18) of segregation with completed suicide. The 207 genes implicated by the shared regions provide a focused set of genes for further study; 18 have been previously associated with suicide risk. Although PsychArray variants do not represent exhaustive variation within the 207 genes, we investigated these for specific segregation within the high-risk families, and for association of variants with predicted functional impact in ~ 1300 additional Utah suicides unrelated to the discovery families. None of the limited PsychArray variants explained the high-risk family segregation; sequencing of these regions will be needed to discover segregating risk variants, which may be rarer or regulatory. However, additional association tests yielded four significant PsychArray variants (SP110, rs181058279; AGBL2, rs76215382; SUCLA2, rs121908538; APH1B, rs745918508), raising the likelihood that these genes confer risk of completed suicide.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

N-Terminal Truncated Myb with New Transcriptional Activity Produced Through Use of an Alternative MYB Promoter in Salivary Gland Adenoid Cystic Carcinoma

Adenoid cystic carcinoma (ACC) is an aggressive salivary gland tumor that frequently displays perineural invasion and is often associated with translocations or overexpression of the MYB oncogene. Detailed analyses of MYB transcripts from ACC patient samples revealed that ACC tumors utilize an alternative MYB promoter, which is rarely used in normal cells or other tumor types. The alternative promoter transcripts produce N-terminally truncated Myb proteins lacking a highly conserved and phosphorylated domain, which includes the pS11 epitope that is frequently used to detect Myb proteins. In RNA-seq assays, Myb isoforms lacking the N-terminal domain displayed unique transcriptional activities, regulating many genes differently than full-length Myb. Thus, a regulatory pathway unique to ACC activates the alternative MYB promoter, leading to the production of a truncated Myb protein with altered transcriptional activities. This could provide new therapeutic opportunities for ACC patients.

Benchmarking network algorithms for contextualizing genes of interest

Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.

In our labs, we aimed to use network algorithms to contextualize hits from functional genomics screens and gene expression studies. In order to understand how to apply these algorithms to our data, we characterized seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input.

A Network-Based Method for Mechanistic Investigation and Neuroprotective Effect on Post-treatment of Senkyunolid-H Against Cerebral Ischemic Stroke in Mouse

Senkyunolide-H (SEH), a major bioactive compound extracted from Ligusticum chuanxiong, has been reported to be effective in preventing cerebral ischemic stroke (CIS). In this study, we employed network pharmacology to reveal potential mechanism of SEH against CIS on a system level and confirmed the therapeutic effects of SEH on CIS by models of cerebral ischemia-reperfusion in vivo and in vitro. Through protein-protein interaction networks construction of SEH- and CIS-related targets, a total of 62 key targets were obtained by screening topological indices and analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment. Gene Ontology analysis indicated that SEH might have a role in treating CIS via regulating some biological processes including regulation of transcription from RNA polymerase II promoter, epidermal growth factor receptor signaling pathway, phosphatidylinositol-mediated signaling, and some molecular function, such as transcription factor and protein phosphatase binding and nitric oxide synthase regulator activity. Meanwhile, the Kyoto Encyclopedia of Genes and Genomes analysis showed that phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway was significantly enriched. In addition, our result showed that SEH posttreatment significantly decreased the neurological scores, infarct volume, and neuronal death in the middle cerebral artery occlusion mice. Moreover, the PI3K/Akt/nuclear factor kappa B signaling pathway was activated by intragastric administration of 40 mg/kg SEH, as verified by Western blot. In vitro, treatment of PC12 cells with 100 μM SEH markedly reduced cell death induced by oxygen-glucose deprivation through the activation of PI3K/Akt/nuclear factor kappa B pathway, and the therapeutic effect of SEH was obviously inhibited by 10 μM LY294002. In summary, these results suggested that SEH carries a therapeutic potential in CIS involving multiple targets and pathways, and the most crucial mechanism might be through the activation of PI3K/Akt/nuclear factor kappa B (NF-κB) signaling pathway to inhibit inflammatory factor releases and increase the antiapoptosis capacity. Our study furnishes the future traditional Chinese medicine research with a network pharmacology framework.

Multivariable regulation of gene expression plasticity in metazoans

An important capacity of genes is the rapid change of expression levels to cope with the environment, known as expression responsiveness or plasticity. Elucidating the genomic mechanisms determining expression plasticity is critical for understanding the molecular basis of phenotypic plasticity, fitness and adaptation. In this study, we systematically quantified gene expression plasticity in four metazoan species by integrating changes of expression levels under a large number of genetic and environmental conditions. From this, we demonstrated that expression plasticity measures a distinct feature of gene expression that is orthogonal to other well-studied features, including gene expression level and tissue specificity/broadness. Expression plasticity is conserved across species with important physiological implications. The magnitude of expression plasticity is highly correlated with gene function and genes with high plasticity are implicated in disease susceptibility. Genome-wide analysis identified many conserved promoter cis-elements, trans-acting factors (such as CTCF), and gene body histone modifications (H3K36me3, H3K79me2 and H4K20me1) that are significantly associated with expression plasticity. Analysis of expression changes in perturbation experiments further validated a causal role of specific transcription factors and histone modifications. Collectively, this work reveals the general properties, physiological implications and multivariable regulation of gene expression plasticity in metazoans, extending the mechanistic understanding of gene regulation.

On the hunt for the alternate host of Hemileia vastatrix

Coffee leaf rust (CLR), caused by the fungal pathogen Hemileia vastatrix, has plagued coffee production worldwide for over 150 years. Hemileia vastatrix produces urediniospores, teliospores, and the sexual basidiospores. Infection of coffee by basidiospores of H. vastatrix has never been reported and thus far, no alternate host, capable of supporting an aecial stage in the disease cycle, has been found. Due to this, some argue that an alternate host of H. vastatrix does not exist. Yet, to date, the plant pathology community has been puzzled by the ability of H. vastatrix to overcome resistance in coffee cultivars despite the apparent lack of sexual reproduction and an aecidial stage. The purpose of this study was to introduce a new method to search for the alternate host(s) of H. vastatrix. To do this, we present the novel hypothetical alternate host ranking (HAHR) method and an automated text mining (ATM) procedure, utilizing comprehensive biogeographical botanical data from the designated sites of interests (Ethiopia, Kenya and Sri Lanka) and plant pathology insights. With the HAHR/ATM methods, we produced prioritized lists of potential alternate hosts plant of coffee leaf rust. This is a first attempt to seek out an alternate plant host of a pathogenic fungus in this manner. The HAHR method showed the highest-ranking probable alternate host as Psychotria mahonii, Rubus apetalus, and Rhamnus prinoides. The cross-referenced results by the two methods suggest that plant genera of interest are Croton, Euphorbia, and Rubus. The HAHR and ATM methods may also be applied to other plant-rust interactions that include an unknown alternate host or any other biological system, which rely on data mining of published data.

Using Network Pharmacology to Explore Potential Treatment Mechanism for Coronary Heart Disease Using Chuanxiong and Jiangxiang Essential Oils in Jingzhi Guanxin Prescriptions

Background

To predict the active components and potential targets of traditional Chinese medicine and to determine the mechanism behind the curative effect of traditional Chinese medicine, a multitargeted method was used. Jingzhi Guanxin prescriptions expressed a high efficacy for coronary heart disease (CHD) patients of which essential oils from Chuanxiong and Jiangxiang were confirmed to be the most important effective substance. However, the interaction between the active components and the targets for the treatment of CHD has not been clearly explained in previous studies.

Materials and Methods

Genes associated with the disease and the treatment strategy were searched from the electronic database and analyzed by Cytoscape (version 3.2.1). Protein-protein interaction network diagram of CHD with Jiangxiang and Chuanxiong essential oils was constructed by Cytoscape. Pathway functional enrichment analysis was executed by clusterProfiler package in R platform.

Results

121 ingredients of Chuanxiong and Jiangxiang essential oils were analyzed, and 393 target genes of the compositions and 912 CHD-related genes were retrieved. 15 coexpression genes were selected, including UGT1A1, DPP4, RXRA, ADH1A, RXRG, UGT1A3, PPARA, TRPC3, CYP1A1, ABCC2, AHR, and ADRA2A. The crucial pathways of occurrence and treatment molecular mechanism of CHD were analyzed, including retinoic acid metabolic process, flavonoid metabolic process, response to xenobiotic stimulus, cellular response to xenobiotic stimulus, cellular response to steroid hormone stimulus, retinoid binding, retinoic acid binding, and monocarboxylic acid binding. Finally, we elucidate the underlying role and mechanism behind these genes in the pathogenesis and treatment of CHD.

Conclusions

Generally speaking, the nodes in subnetwork affect the pathological process of CHD, thus indicating the mechanism of Jingzhi Guanxin prescriptions containing Chuanxiong and Jiangxiang essential oils in the treatment of CHD.

Circ_MDM2_000139, Circ_ATF2_001418, Circ_CDC25C_002079, and Circ_BIRC6_001271 Are Involved in the Functions of XAV939 in Non-Small Cell Lung Cancer

Background

The small molecule inhibitor XAV939 could inhibit the proliferation and promote the apoptosis of non-small cell lung cancer (NSCLC) cells. This study was conducted to identify the key circular RNAs (circRNAs) and microRNAs (miRNAs) in XAV939-treated NSCLC cells.

Methods

After grouping, the NCL-H1299 cells in the treatment group were treated by 10 μM XAV939 for 12 h. RNA-sequencing was performed, and then the differentially expressed circRNAs (DE-circRNAs) were analyzed by the edgeR package. Using the clusterprofiler package, enrichment analysis for the hosting genes of the DE-circRNAs was performed. Using Cytoscape software, the miRNA-circRNA regulatory network was built for the disease-associated miRNAs and the DE-circRNAs. The DE-circRNAs that could translate into proteins were predicted using circBank database and IRESfinder tool. Finally, the transcription factor (TF)-circRNA regulatory network was built by Cytoscape software. In addition, A549 and HCC-827 cell treatment with XAV939 were used to verify the relative expression levels of key DE-circRNAs.

Results

There were 106 DE-circRNAs (including 61 upregulated circRNAs and 45 downregulated circRNAs) between treatment and control groups. Enrichment analysis for the hosting genes of the DE-circRNAs showed that ATF2 was enriched in the TNF signaling pathway. Disease association analysis indicated that 8 circRNAs (including circ_MDM2_000139, circ_ATF2_001418, circ_CDC25C_002079, and circ_BIRC6_001271) were correlated with NSCLC. In the miRNA-circRNA regulatory network, let-7 family members⟶circ_MDM2_000139, miR-16-5p/miR-134-5p⟶circ_ATF2_001418, miR-133b⟶circ_BIRC6_001271, and miR-221-3p/miR-222-3p⟶circ_CDC25C_002079 regulatory pairs were involved. A total of 47 DE-circRNAs could translate into proteins. Additionally, circ_MDM2_000139 was targeted by the TF POLR2A. The verification test showed that the relative expression levels of circ_MDM2_000139, circ_CDC25C_002079, circ_ATF2_001418, and circ_DICER1_000834 in A549 and HCC-827 cell treatment with XAV939 were downregulated comparing with the control.

Conclusions

Let-7 family members and POLR2A targeting circ_MDM2_000139, miR-16-5p/miR-134-5p targeting circ_ATF2_001418, miR-133b targeting circ_BIRC6_001271, and miR-221-3p/miR-222-3p targeting circ_CDC25C_002079 might be related to the mechanism in the treatment of NSCLC by XAV939.

Cross-disorder analysis of endometriosis and its comorbid diseases reveals shared genes and molecular pathways and proposes putative biomarkers of endometriosis

RESEARCH QUESTION

Women with endometriosis are considered to be at higher risk of several chronic diseases, such as autoimmune disorders, gynaecological cancers, asthma/atopic diseases and cardiovascular and inflammatory bowel diseases. Could the study of endometriosis-associated comorbidities help to identify potential biomarkers and target pathways of endometriosis?

DESIGN

A systematic review was performed to identify all possible endometriosis-associated comorbid conditions. Next, this list of disorders was coded into MeSH terms, and the gene expression profiles were downloaded from the Phenopedia database and subsequently analysed following a systems biology approach.

RESULTS

The results identified a group of 127 candidate genes that were recurrently expressed in endometriosis and its closest comorbidities and that were defined as 'endometriosis sibling disorders' (ESD). The enrichment analysis showed that these candidate genes are principally involved in immune and drug responses, hormone metabolism and cell proliferation, which are well-known hallmarks of endometriosis. The expression of ESD genes was then validated on independent sample cohorts (n = 207 samples), in which the involvement of 16 genes (AGTR1, BDNF, C3, CCL2, CD40, CYP17A1, ESR1, IGF1, IGF2, IL10, MMP1, MMP7, MMP9, PGR, SERPINE1 and TIMP2) in endometriosis was confirmed. Several of these genes harbour polymorphisms that associate to either endometriosis or its comorbid conditions.

CONCLUSIONS

The study results highlight the molecular processes underlying the aetiopathogenesis of endometriosis and its comorbid conditions, and identify putative endometriosis biomarkers.

A Parametric Targetability Evaluation Approach for Vitiligo Proteome Extracted through Integration of Gene Ontologies and Protein Interaction Topologies

Vitiligo is a well-known skin disorder with complex etiology. Vitiligo pathogenesis is multifaceted with many ramifications. A computational systemic path was designed to first propose candidate disease proteins by merging properties from protein interaction networks and gene ontology terms. All in all, 109 proteins were identified and suggested to be involved in the onset of disease or its progression. Later, a composite approach was employed to prioritize vitiligo disease proteins by comparing and benchmarking the properties against standard target identification criteria. This includes sequence-based, structural, functional, essentiality, protein-protein interaction, vulnerability, secretability, assayability, and druggability information. The existing information was seamlessly integrated into efficient pipelines to propose a novel protocol for assessment of targetability of disease proteins. Using the online data resources and the scripting, an illustrative list of 68 potential drug targets was generated for vitiligo. While this list is broadly consistent with the research community's current interest in certain specific proteins, and suggests novel target candidates that may merit further study, it can still be modified to correspond to a user-specific environment, either by adjusting the weights for chosen criteria (i.e., a quantitative approach) or by changing the considered criteria (i.e., a qualitative approach).

Based on network pharmacology to explore the molecular mechanisms of astragalus membranaceus for treating T2 diabetes mellitus

Background

Astragalus membranaceus refers to a type of traditional Chinese medicine (TCM) used to treat type 2 diabetes mellitus (T2DM), whereas its molecular mechanism remains unclear. In the presented study, network pharmacology was performed to analyze the molecular mechanism of astragalus membranaceus against T2DM.

Methods

First, we found common targets of astragalus membranaceus and disease, protein-protein interaction (PPI) network was built by String, and then key targets were screened from these common targets by topological analysis. Subsequently, common targets were introduced into DAVID to achieve the results of gene ontology (GO) and KEGG enrichment analysis. The therapeutic effect of astragalus was observed, and several key targets were verified by an animal experiment.

Results

First, 13 key targets (EGFR, KDR, SRC, ERBB2, FYN, ESR1, AR, HSP90AA1, PTGS2, ABCG2, AB1, MMP2, and CYP1) were found by topological analysis. Then, the results of GO and KEGG suggested that the anti-diabetes effect of astragalus membranaceus was strongly associated with the activation of receptor protein tyrosine kinase (RPTK). The results of animal experiments revealed that astragalus could enhance the morphology of rat pancreas and up-regulate the expression of tyrosine receptor.

Conclusions

In brief, 13 key targets were found in this study, and astragalus membranaceus was found up-regulating insulin signaling pathways by improving the activity of casein kinase, regulating lipid metabolism, and enhancing insulin resistance to treat T2DM. The present study lays a basis for subsequent experimental research and broadens the clinical application of astragalus membranaceus.

An integrated epigenome and transcriptome analysis identifies PAX2 as a master regulator of drug resistance in high grade pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is notoriously difficult to treat due to its aggressive, ever resilient nature. A major drawback lies in its tumor grade; a phenomenon observed across various carcinomas, where highly differentiated and undifferentiated tumor grades, termed as low and high grade respectively, are found in the same tumor. One eminent problem due to such heterogeneity is drug resistance in PDAC. This has been implicated to ABC transporter family of proteins that are upregulated in PDAC patients. However, the regulation of these transporters with respect to tumor grade in PDAC is not well understood. To combat these issues, a study was designed to identify novel genes that might regulate drug resistance phenotype and be used as targets. By integrating epigenome with transcriptome data, several genes were identified based around high grade PDAC. Further analysis indicated oncogenic PAX2 transcription factor as a novel regulator of drug resistance in high grade PDAC cell lines. It was observed that silencing of PAX2 resulted in increased susceptibility of high grade PDAC cells to various chemotherapeutic drugs. Mechanistically, the study showed that PAX2 protein can bind and alter transcriptionally; expression of many ABC transporter genes in high grade PDAC cell lines. Overall, the study indicated that PAX2 significantly upregulated ABC family of genes resulting in drug resistance and poor survival in PDAC.

Investigating the Multi-Target Pharmacological Mechanism of Hedyotis diffusa Willd Acting on Prostate Cancer: A Network Pharmacology Approach

Hedyotis diffusa Willd (HDW) is one of the most well-known herbs used in the treatment of prostate cancer. However, the potential mechanisms of its anti-tumor effects have not been fully explored. Here, we applied a network pharmacology approach to explore the potential mechanisms of HDW against prostate cancer (PCa). We obtained 14 active compounds from HDW and 295 potential PCa related targets in total to construct a network, which indicated that quercetin and ursolic acid served as the main ingredients in HDW. Mitogen-activated Protein Kinase 8 (MAPK8), Interleukin 6 (IL6), Vascular Endothelial Growth Factor A (VEGFA), Signal Transducer and Activator of Transcription 3 (STAT3), Jun Proto-Oncogene (JUN), C-X-C Motif Chemokine Ligand 8 (CXCL8), Interleukin-1 Beta (IL1B), Matrix Metalloproteinase-9 (MMP9), C-C Motif Chemokine Ligand 2 (CCL2), RELA Proto-Oncogene (RELA), and CAMP Responsive Element Binding Protein 1 (CREB1) were identified as key targets of HDW in the treatment of PCa. The protein-protein interaction (PPI) cluster demonstrated that CREB1 was the seed in this cluster, indicating that CREB1 plays an important role in connecting other nodes in the PPI network. This enrichment demonstrated that HDW was highly related to translesion synthesis, unfolded protein binding, regulation of mitotic recombination, phosphatidylinositol and its kinase-mediated signaling, nucleotide excision repair, regulation of DNA recombination, and DNA topological change. The enrichment results also showed that the underlying mechanism of HDW against PCa may be due to its coordinated regulation of several cancer-related pathways, such as angiogenesis, cell differentiation, migration, apoptosis, invasion, and proliferation.

A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases

Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.

Model annotation and discovery with the Physiome Model Repository

BACKGROUND

Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner.

RESULTS

We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use.

CONCLUSION

The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy

Epilepsy is a common neurological disorder that affects mammalian species including human beings and dogs. In order to discover novel drugs for the treatment of canine epilepsy, multiomics data were analyzed to identify epilepsy associated genes. In this research, the original ranking of associated genes was obtained by two high-throughput bioinformatics experiments: Genome Wide Association Study (GWAS) and microarray analysis. The association ranking of genes was enhanced by a re-ranking system, HPO-Shuffle, which integrated information from GWAS, microarray analysis and Human Phenotype Ontology database for further downstream analysis. After applying HPO-Shuffle, the association ranking of epilepsy genes were improved. Afterward, a weighted gene coexpression network analysis (WGCNA) led to a set of gene modules, which hinted a clear relevance between the high ranked genes and the target disease. Finally, WGCNA and connectivity map (CMap) analysis were performed on the integrated dataset to discover a potential drug list, in which a well-known anticonvulsant phensuximide was included.

Potentially repurposable drugs for schizophrenia identified from its interactome

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.

Astrocytes are direct cellular targets of lithium treatment: novel roles for lysyl oxidase and peroxisome-proliferator activated receptor-γ as astroglial targets of lithium

Astrocytes are multifunctional glial cells that play essential roles in supporting synaptic signalling and white matter-associated connectivity. There is increasing evidence that astrocyte dysfunction is involved in several brain disorders, including bipolar disorder (BD), depression and schizophrenia. The mood stabiliser lithium is a frontline treatment for BD, but the mechanisms of action remain unclear. Here, we demonstrate that astrocytes are direct targets of lithium and identify unique astroglial transcriptional networks that regulate specific molecular changes in astrocytes associated with BD and schizophrenia, together with Alzheimer's disease (AD). Using pharmacogenomic analyses, we identified novel roles for the extracellular matrix (ECM) regulatory enzyme lysyl oxidase (LOX) and peroxisome proliferator-activated receptor gamma (PPAR-γ) as profound regulators of astrocyte morphogenesis. This study unravels new pathophysiological mechanisms in astrocytes that have potential as novel biomarkers and potential therapeutic targets for regulating astroglial responses in diverse neurological disorders.

A Radiogenomic Approach for Decoding Molecular Mechanisms Underlying Tumor Progression in Prostate Cancer

Prostate cancer (PCa) is a genetically heterogeneous cancer entity that causes challenges in pre-treatment clinical evaluation, such as the correct identification of the tumor stage. Conventional clinical tests based on digital rectal examination, Prostate-Specific Antigen (PSA) levels, and Gleason score still lack accuracy for stage prediction. We hypothesize that unraveling the molecular mechanisms underlying PCa staging via integrative analysis of multi-OMICs data could significantly improve the prediction accuracy for PCa pathological stages. We present a radiogenomic approach comprising clinical, imaging, and two genomic (gene and miRNA expression) datasets for 298 PCa patients. Comprehensive analysis of gene and miRNA expression profiles for two frequent PCa stages (T2c and T3b) unraveled the molecular characteristics for each stage and the corresponding gene regulatory interaction network that may drive tumor upstaging from T2c to T3b. Furthermore, four biomarkers (ANPEP, mir-217, mir-592, mir-6715b) were found to distinguish between the two PCa stages and were highly correlated (average r = ± 0.75) with corresponding aggressiveness-related imaging features in both tumor stages. When combined with related clinical features, these biomarkers markedly improved the prediction accuracy for the pathological stage. Our prediction model exhibits high potential to yield clinically relevant results for characterizing PCa aggressiveness.

Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information

Motivation

Most gene prioritization methods model each disease or phenotype individually, but this fails to capture patterns common to several diseases or phenotypes. To overcome this limitation, we formulate the gene prioritization task as the factorization of a sparsely filled gene-phenotype matrix, where the objective is to predict the unknown matrix entries. To deliver more accurate gene-phenotype matrix completion, we extend classical Bayesian matrix factorization to work with multiple side information sources. The availability of side information allows us to make non-trivial predictions for genes for which no previous disease association is known.

Results

Our gene prioritization method can innovatively not only integrate data sources describing genes, but also data sources describing Human Phenotype Ontology terms. Experimental results on our benchmarks show that our proposed model can effectively improve accuracy over the well-established gene prioritization method, Endeavour. In particular, our proposed method offers promising results on diseases of the nervous system; diseases of the eye and adnexa; endocrine, nutritional and metabolic diseases; and congenital malformations, deformations and chromosomal abnormalities, when compared to Endeavour.

Availability and implementation

The Bayesian data fusion method is implemented as a Python/C++ package: https://github.com/jaak-s/macau. It is also available as a Julia package: https://github.com/jaak-s/BayesianDataFusion.jl. All data and benchmarks generated or analyzed during this study can be downloaded at https://owncloud.esat.kuleuven.be/index.php/s/UGb89WfkZwMYoTn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Evolutionary history of disease-susceptibility loci identified in longitudinal exome-wide association studies

Background

Our longitudinal exome‐wide association studies previously detected various genetic determinants of complex disorders using ~26,000 single‐nucleotide polymorphisms (SNPs) that passed quality control and longitudinal medical examination data (mean follow‐up period, 5 years) in 4884–6022 Japanese subjects. We found that allele frequencies of several identified SNPs were remarkably different among four ethnic groups. Elucidating the evolutionary history of disease‐susceptibility loci may help us uncover the pathogenesis of the related complex disorders.

Methods

In the present study, we conducted evolutionary analyses such as extended haplotype homozygosity, focusing on genomic regions containing disease‐susceptibility loci and based on genotyping data of our previous studies and datasets from the 1000 Genomes Project.

Results

Our evolutionary analyses suggest that derived alleles of rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs closely located at 12q24.1 associated with type 2 diabetes mellitus, obesity, dyslipidemia, and three complex disorders (hypertension, hyperuricemia, and dyslipidemia), respectively, rapidly expanded after the human dispersion from Africa (Out‐of‐Africa). Allele frequencies of GGA3 and six SNPs at 12q24.1 appeared to have remarkably changed in East Asians, whereas the derived alleles of rs34902660 of SLC17A3 and rs7656604 at 4q13.3 might have spread across Japanese and non‐Africans, respectively, although we cannot completely exclude the possibility that allele frequencies of disease‐associated loci may be affected by demographic events.

Conclusion

Our findings indicate that derived allele frequencies of nine disease‐associated SNPs (rs78338345 of GGA3, rs7656604 at 4q13.3, rs34902660 of SLC17A3, and six SNPs at 12q24.1) identified in the longitudinal exome‐wide association studies largely increased in non‐Africans after Out‐of‐Africa.

eRAM: encyclopedia of rare disease annotations for precision medicine

Rare diseases affect over a hundred million people worldwide, most of these patients are not accurately diagnosed and effectively treated. The limited knowledge of rare diseases forms the biggest obstacle for improving their treatment. Detailed clinical phenotyping is considered as a keystone of deciphering genes and realizing the precision medicine for rare diseases. Here, we preset a standardized system for various types of rare diseases, called encyclopedia of Rare disease Annotations for Precision Medicine (eRAM). eRAM was built by text-mining nearly 10 million scientific publications and electronic medical records, and integrating various data in existing recognized databases (such as Unified Medical Language System (UMLS), Human Phenotype Ontology, Orphanet, OMIM, GWAS). eRAM systematically incorporates currently available data on clinical manifestations and molecular mechanisms of rare diseases and uncovers many novel associations among diseases. eRAM provides enriched annotations for 15 942 rare diseases, yielding 6147 human disease related phenotype terms, 31 661 mammalians phenotype terms, 10,202 symptoms from UMLS, 18 815 genes and 92 580 genotypes. eRAM can not only provide information about rare disease mechanism but also facilitate clinicians to make accurate diagnostic and therapeutic decisions towards rare diseases. eRAM can be freely accessed at http://www.unimd.org/eram/.

Discovery of stroke-related blood biomarkers from gene expression network models

BACKGROUND

Identifying molecular biomarkers characteristic of ischemic stroke has the potential to aid in distinguishing stroke cases from stroke mimicking symptoms, as well as advancing the understanding of the physiological changes that underlie the body's response to stroke. This study uses machine learning-based analysis of gene co-expression to identify transcription patterns characteristic of patients with acute ischemic stroke.

METHODS

Mutual information values for the expression levels among 13,243 quantified transcripts were computed for blood samples from 82 stroke patients and 68 controls to construct a co-expression network of genes (separately) for stroke and control samples. Page rank centrality scores were computed for every gene; a gene's significance in the network was assessed according to the differences in their network's pagerank centrality between stroke and control expression patterns. A hybrid genetic algorithm - support vector machine learning tool was used to classify samples based on gene centrality in order to identify an optimal set of predictor genes for stroke while minimizing the number of genes in the model.

RESULTS

A predictive model with 89.6% accuracy was identified using 6 network-central and differentially expressed genes (ID3, MBTPS1, NOG, SFXN2, BMX, SLC22A1), characterized by large differences in association network connectivity between stroke and control samples. In contrast, classification models based solely on individual genes identified by significant fold-changes in expression level provided lower predictive accuracies: < 71% for any single gene, and even models with larger (10-25) numbers of gene transcript biomarkers gave lower predictive accuracies (≤ 82%) than the 6 network-based gene signature classification. miRNA:mRNA target prediction computational analysis revealed 8 differentially expressed micro-RNAs (miRNAs) that are significantly associated with at least 2 of the 6 network-central genes.

CONCLUSIONS

Network-based models have the potential to identify a more statistically robust pattern of gene expression typical of acute ischemic stroke and to generate hypotheses about possible interactions among functionally relevant genes, leading to the identification of more informative biomarkers.

Downregulation of microRNA-205 inhibits cell invasion and angiogenesis of cervical cancer through TSLC1-mediated Akt signaling pathway

Cervical cancer (CC) is a common gynecological cancer and a leading cause of cancer-related deaths in women globally. Therefore, this study explores the action of microRNA-205 (miR-205) in the invasion, migration, and angiogenesis of CC through binding to tumor suppressor lung cancer 1 (TSLC1). Initially, the microarray analysis was used to select the candidate gene and the regulatory microRNA. Then, the target relationship between miR-205 and TSLC1 as well as the expression of miR-205, TSLC1, and p-Akt/total Akt in CC cells were determined. Afterwards, CC cell invasion and migration were detected after the treatment of miR-205 mimics/inhibitors and short hairpin RNA against TSLC1. After coculture of cancer cells and vascular endothelial cells, cell proliferation, tube formation, and microvessel density (MVD) were detected to determine the roles of miR-205 in angiogenesis. Finally, tumor growth in nude mice was measured in vivo. TSLC1 was determined as the candidate gene, which was found to be targeted and negatively regulated by miR-205. Then, downregulated miR-205 or forced TSLC1 expression inhibited invasion, migration, and angiogenesis in CC, corresponding to suppressed cell proliferation, tube formation, and expression of IL-8, VEGF, and bFGF, as well as the inhibited activation of the Akt signaling pathway. Furthermore, downregulation of miR-205 was found to exert an inhibitory role in tumor formation and MVD by elevating TSLC1 in CC in vivo. This study demonstrated that downregulated miR-205 inhibited cell invasion, migration, and angiogenesis in CC by inactivating the Akt signaling pathway via TSLC1 upregulation.

Systematically characterize the substance basis of Jinzhen oral liquid and their pharmacological mechanism using UPLC-Q-TOF/MS combined with network pharmacology analysis

Jinzhen oral liquid (JZ) is a classical traditional Chinese medicine formula used for the treatment of children lung disease. However, the effective substance of JZ is still unclear. In this study, we used lung injury rat model to study the protective effect of JZ, through UPLC-Q-TOF/MS detection coupled with metabolic research and network pharmacology analysis. Fortunately, 31 absorbed prototype constituents and 41 metabolites were identified or tentatively characterized based on UPLC-Q-TOF/MS analysis, and the possible metabolic pathways were hydroxylation, sulfation and glucuronidation. We optimized the data screening in the early stage of network pharmacology by collecting targets based on adsorbed constituents, and further analyzed the main biological processes and pathways. 24 selected core targets were frequently involved in reactive oxygen species metabolic process, dopaminergic synapse pathway and so on, which might play important roles in the mechanisms of JZ for the treatment of lung injury. Overall, the absorbed constituents and their possible metabolic pathways, as well as the absorbed constituent-target-disease network provided insights into the mechanisms of JZ for the treatment of lung injury. Further studies are needed to validate the biological processes and effect pathways of JZ.

RITAN: rapid integration of term annotation and network resources

Background

Identifying the biologic functions of groups of genes identified in high-throughput studies currently requires considerable time and/or bioinformatics experience. This is due in part to each resource housed within separate databases, requiring users to know about them, and integrate across them. Time consuming and often repeated for each study, integrating across resources and merging with data under study is an increasingly common bioinformatics task.

Methods

We developed an open-source R software package for assisting researchers in annotating their genesets with functions, pathways, and their interconnectivity across a diversity of network resources.

Results

We present rapid integration of term annotation and network resources (RITAN) for the rapid and comprehensive annotation of a list of genes using functional term and pathway resources and their relationships among each other using multiple network biology resources. Currently, and to comply with data redistribution policies, RITAN allows rapid access to 16 term annotations spanning gene ontology, biologic pathways, and immunologic modules, and nine network biology resources, with support for user-supplied resources; we provide recommendations for additional resources and scripts to facilitate their addition to RITAN. Having the resources together in the same system allows users to derive novel combinations. RITAN has a growing set of tools to explore the relationships within resources themselves. These tools allow users to merge resources together such that the merged annotations have a minimal overlap with one another. Because we index both function annotation and network interactions, the combination allows users to expand small groups of genes using links from biologic networks—either by adding all neighboring genes or by identifying genes that efficiently connect among input genes—followed by term enrichment to identify functions. That is, users can start from a core set of genes, identify interacting genes from biologic networks, and then identify the functions to which the expanded list of genes contribute.

Conclusion

We believe RITAN fills the important niche of bridging the results of high-throughput experiments with the ever-growing corpus of functional annotations and network biology resources.

Availability

Rapid integration of term annotation and network resources is available as an R package at github.com/MTZimmer/RITAN and BioConductor.org.

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data collected from biomedical research is expanding at an almost exponential rate, our ability to transform that information into patient care has not kept at pace. A major barrier preventing this transformation is that multi-dimensional data collection and analysis is usually carried out without much understanding of the underlying knowledge structure. Here, in an effort to bridge this gap, Electronic Health Records (EHRs) of individual patients are connected to a heterogeneous knowledge network called Scalable Precision Medicine Oriented Knowledge Engine (SPOKE). Then an unsupervised machine-learning algorithm creates Propagated SPOKE Entry Vectors (PSEVs) that encode the importance of each SPOKE node for any code in the EHRs. We argue that these results, alongside the natural integration of PSEVs into any EHR machine-learning platform, provide a key step toward precision medicine.

Prioritizing target-disease associations with novel safety and efficacy scoring methods

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

GATA1 gene silencing inhibits invasion, proliferation and migration of cholangiocarcinoma stem cells via disrupting the PI3K/AKT pathway

Background/aims: Intrahepatic cholangiocarcinoma (CCA) is the second most prevalent type primary liver malignancy, accompanied by an increasing global incidence and mortality rate. Research has documented the contribution of the GATA binding protein-1 (GATA1) in the progression of liver cancer. Here, we aim to investigate the role of GATA1 in CCA stem cells via the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway.

Methods: Initially, microarray-based gene expression profiling was employed to identify the differentially expressed genes associated with CCA. Subsequently, an investigation was conducted to explore the potential biological significance behind the silencing of GATA1 and the regulatory mechanism between GATA1 and PI3K/AKT pathway. CCA cell lines QBC-939 and RBE were selected and treated with siRNA against GATA1 or/and a PI3K/AKT pathway inhibitor LY294002. In vivo experiment was also conducted to confirm in vitro findings.

Results: GATA1 exhibited higher expression in CCA samples and was predicted to affect the progression of CCA through blockade of the PI3K/AKT pathway. siRNA-mediated downregulation of GATA1 and LY294002 treatment resulted in reduced proliferation, migration and invasion abilities of CCA stem cells, together with impeded tumor growth, and led to increased cell apoptosis and primary cilium expression. Additionally, the siRNA-mediated GATA1 downregulation had an inhibitory effect on the PI3K/AKT pathway. LY294002 was manifested to enhance the inhibitory effects of GATA1 inhibition on CCA progression. These in vitro findings were reproduced in vivo on siRNA against GATA1 or LY294002 injected nude mice.

Conclusion: Altogether, the present study highlighted that downregulation of GATA1 via blockade of the PI3K/AKT pathway could inhibit the CCA stem cell proliferation, migration and invasion, and tumor growth, and promote cell apoptosis, primary cilium expression.

NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

Matrix factorization-based data fusion for the prediction of lncRNA-disease associations

Motivation

Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be.

Results

To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities.

Availability and implementation

The source code for MFLDA is available at: http://mlda.swu.edu.cn/codes.php? name = MFLDA.

Contact

gxyu@swu.edu.cn.

Supplementary information

Supplementary data are available at Bioinformatics online.

Gene ORGANizer: linking genes to the organs they affect

One of the biggest challenges in studying how genes work is understanding their effect on the physiology and anatomy of the body. Existing tools try to address this using indirect features, such as expression levels and biochemical pathways. Here, we present Gene ORGANizer (geneorganizer.huji.ac.il), a phenotype-based tool that directly links human genes to the body parts they affect. It is built upon an exhaustive curated database that links >7000 genes to ∼150 anatomical parts using >150 000 gene-organ associations. The tool offers user-friendly platforms to analyze the anatomical effects of individual genes, and identify trends within groups of genes. We demonstrate how Gene ORGANizer can be used to make new discoveries, showing that chromosome X is enriched with genes affecting facial features, that positive selection targets genes with more constrained phenotypic effects, and more. We expect Gene ORGANizer to be useful in a variety of evolutionary, medical and molecular studies aimed at understanding the phenotypic effects of genes.

MiR-21 binding site SNP within ITGAM associated with psoriasis susceptibility in women

BACKGROUND

Great progress has been made in the understanding of inflammatory processes in psoriasis. However, clarifying the role of genetic variability in processes regulating inflammation, including post-transcriptional regulation by microRNA (miRNA), remains a challenge.

OBJECTIVES

We therefore investigated single nucleotide polymorphisms (SNPs) with a predicted change in the miRNA/mRNA interaction of genes involved in the psoriasis inflammatory processes.

METHODS

Studied SNPs rs2910164 C/G-miR-146a, rs4597342 T/C-ITGAM, rs1368439 G/T-IL12B, rs1468488 C/T-IL17RA were selected using a bioinformatics analysis of psoriasis inflammation-associated genes. These SNPs were then genotyped using a large cohort of women with psoriasis (n = 241) and healthy controls (n = 516).

RESULTS

No significant association with psoriasis was observed for rs2910164, rs1368439, and rs1468488 genotypes. However, the major allele T of rs4597342 -ITGAM was associated with approximately 28% higher risk for psoriasis in comparison to the patients with the C allele (OR = 1.28, 95% CI 1.01-1.61, p = 0.037). In case of genotypes, the effect of the T allele indicates the dominant model of disease penetrance as the CT and TT genotypes increase the chance of psoriasis up to 42% in comparison to CC homozygotes of rs4597342 (OR = 1.42, 95% CI = 1.05-1.94, p = 0.025).

CONCLUSION

SNP rs4597342 in 3'UTR of ITGAM influencing miR-21 binding may be considered a risk factor for psoriasis development. Upregulated miR-21 in psoriasis is likely to inhibit CD11b production in the case of the rs4597342 T allele which may lead to Mac-1 dysfunction, resulting in an aberrant function of innate immune cells and leading to the production of cytokines involved in psoriasis pathogenesis.