Gendoo: functional profiling of gene and disease features using MeSH vocabulary

TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2 Inhibitor Discovery

Target identification is a critical stage in the drug discovery pipeline. Various computational methodologies have been dedicated to enhancing the classification performance of compound-target interactions, yet significant room remains for improving the recommendation performance. To address this challenge, we developed TarIKGC, a tool for target prioritization that leverages semantics enhanced knowledge graph (KG) completion. This method harnesses knowledge representation learning within a heterogeneous compound-target-disease network. Specifically, TarIKGC combines an attention-based aggregation graph neural network with a multimodal feature extractor network to simultaneously learn internal semantic features from biomedical entities and topological features from the KG. Furthermore, a KG embedding model is employed to identify missing relationships among compounds and targets. In silico evaluations highlighted the superior performance of TarIKGC in drug repositioning tasks. In addition, TarIKGC successfully identified two potential cyclin-dependent kinase 2 (CDK2) inhibitors with novel scaffolds through reverse target fishing. Both compounds exhibited antiproliferative activities across multiple therapeutic indications targeting CDK2.

Comparison of Jinzhen oral liquid and ambroxol hydrochloride and clenbuterol hydrochloride oral solution in the treatment of acute bronchitis in children: A multicenter, non-inferiority, prospective, randomized controlled trial

The comparison between traditional Chinese medicine Jinzhen oral liquid (JZOL) and Western medicine in treating children with acute bronchitis (AB) showed encouraging outcomes. This trial evaluated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB. 480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 days. The primary outcome was time-to-cough resolution. The median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 day. This randomized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discomfort symptoms. Combined with clinical trials, the mechanism of JZOL against AB was uncovered by network target analysis, it was found that the pathways in TRP channels like IL-1β/IL1R/TRPV1/TRPA1, NGF/TrkA/TRPV1/TRPA1, and PGE2/EP/PKA/TRPV1/TRPA1 might play important roles. Animal experiments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer

Background: Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. Methods: In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. Results: The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. Conclusions: The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on Network target analysis

OBJECTIVE

The purpose of this study is to reveal the mechanism of action of Yi Qi Tong Qiao Pill (YQTQP) in the treatment of allergic rhinitis (AR), as well as establish a paradigm for the researches on traditional Chinese medicine (TCM) from systematic perspective.

METHODS

Based on the data collected from TCM-related and disease-related databases, target profiles of compounds in YQTQP were calculated through network-based algorithms and holistic targets of TQTQP was constructed. Network target analysis was performed to explore the potential mechanisms of YQTQP in the treatment of AR and the mechanisms were classified into different modules according to their biological functions. Besides, animal and clinical experiments were conducted to validate our findings inferred from Network target analysis.

RESULTS

Network target analysis showed that YQTQP targeted 12 main pathways or biological processes related to AR, represented by those related to IL-4, IFN-γ, TNF-α and IL-13. These results could be classified into 3 biological modules, including regulation of immune and inflammation, epithelial barrier disorder and cell adhesion. Finally, a series of experiments composed of animal and clinical experiments, proved our findings and confirmed that YQTQP could improve related symptoms of AR, like permeability of nasal mucosa epithelium.

CONCLUSION

A combination of Network target analysis and the experimental validation indicated that YQTQP was effective in the treatment of AR and might provide a new insight on revealing the mechanism of TCM against diseases. Trial registration Name of the registry: Chinese Clinical Trial Registry: Trial registration number: ChiCTR-TRC-13,003,137: Date of registration: Registered 29 March 2013 - Retrospectively registered: URL of trial registry record: https://www.chictr.org.cn/showproj.html?proj=6422 .

Exploring the effect of Weifuchun capsule on the toll-like receptor pathway mediated HES6 and immune regulation against chronic atrophic gastritis

ETHNOPHARMACOLOGICAL RELEVANCE

Weifuchun capsule (WFC) is a traditional Chinese patent medicine for chronic atrophic gastritis (CAG) in clinic. However, the mechanism of action of WFC for CAG still remains unclear due to its complex composition.

AIM OF THE STUDY

The study was projected to uncover the mechanism of action of WFC and the corresponding pharmacodynamic substance of WFC against CAG as well as providing a standard example for the research of traditional Chinese medicine (TCM) from the perspective of the network and the system.

MATERIALS AND METHODS

We identified the compounds of WFC through LC-MS/MS analysis and performed a systematic network targets analysis for WFC in the treatment of CAG which thoroughly described the mechanism of action of WFC for CAG. Based on analysis integrating omics data and algorithms, we focused on the specific immune regulatory role of WFC in the treatment of CAG, especially on a hub pathway, Toll-like receptor signaling pathway and thus deciphered the role of WFC in immune regulation, anti-inflammation and mediation of HES6. In experiments part, MNNG-GES-1-cell line and rat models were used to validate our findings.

RESULTS

In this study, compounds of WFC are identified through LC‒MS/MS and network target analysis is performed to dissect the specific immunoregulatory effect as well as mediation of HES6, a newly discovered biomolecule related to gastritis carcinoma progression, of WFC on CAG through the Toll-like receptor signaling pathway. Based on cell line and rat models, we verify the mechanism of action of WFC for CAG in inhibiting inflammatory cytokines, regulating immune cells like T cells and macrophages, related genes including TLR2 and CD14. It is also validated that WFC inhibits the expression of HES6 (P < 0.05).

CONCLUSION

Based on the combination of computational strategy and experiments, our study offers a comprehensive analysis to reveal the role of WFC in regulating immune response, inhibiting inflammation in the treatment of CAG, and provides a standard example for the research of TCM from the perspective of the network and the system.

HFIP: an integrated multi-omics data and knowledge platform for the precision medicine of heart failure

As the terminal clinical phenotype of almost all types of cardiovascular diseases, heart failure (HF) is a complex and heterogeneous syndrome leading to considerable morbidity and mortality. Existing HF-related omics studies mainly focus on case/control comparisons, small cohorts of special subtypes, etc., and a large amount of multi-omics data and knowledge have been generated. However, it is difficult for researchers to obtain biological and clinical insights from these scattered data and knowledge. In this paper, we built the Heart Failure Integrated Platform (HFIP) for data exploration, fusion analysis and visualization by collecting and curating existing multi-omics data and knowledge from various public sources and also provided an auto-updating mechanism for future integration. The developed HFIP contained 253 datasets (7842 samples), multiple analysis flow, and 14 independent tools. In addition, based on the integration of existing databases and literature, a knowledge base for HF was constructed with a scoring system for evaluating the relationship between molecular signals and HF. The knowledge base includes 1956 genes and annotation information. The literature mining module was developed to assist the researcher to overview the hotspots and contexts in basic and clinical research. HFIP can be used as a data-driven and knowledge-guided platform for the basic and clinical research of HF.

Database URL: http://heartfailure.medical-bigdata.com

Distinct genetic regions are associated with differential population susceptibility to chemical exposures

Interactions between environmental factors and genetics underlie the majority of chronic human diseases. Chemical exposures are likely an underestimated contributor, yet gene-environment (GxE) interaction studies rarely assess their modifying effects. Here, we describe a novel method to profile the human genome and identify regions associated with differential population susceptibility to chemical exposures. Single nucleotide polymorphisms (SNPs) implicated in enriched chemical-disease intersections were identified and validated for three chemical classes with expected GxE interaction potential (neuroactive, hepatoactive, and cardioactive compounds). The same approach was then used to characterize consumer product classes with unknown risk for GxE interactions (washing products, cosmetics, and adhesives). Additionally, high-risk variant sets that may confer differential population susceptibility were identified for these consumer product groups through frequent itemset mining and pathway analysis. A dataset of 2454 consumer product chemical-disease linkages, with risk values, SNPs, and pathways for each association was developed, describing the interplay between environmental factors and genetics in human disease progression. We found that genetic hotspots implicated in GxE interactions differ across chemical classes (e.g., washing products had high-risk SNPs implicated in nervous system disease) and illustrate how this approach can discover new associations (e.g., washing product n-butoxyethanol implicated SNPs in the PI3K-Akt signaling pathway for Alzheimer's disease). Hence, our approach can predict high-risk genetic regions for differential population susceptibility to chemical exposures and characterize chemical modifying factors in specific diseases. These methods show promise for describing how chemical exposures can lead to varied health outcomes in a population and for incorporating inter-individual variability into chemical risk assessment.

Phosphorylated proteome analysis of a novel germline ABL1 mutation causing an autosomal dominant syndrome with ventricular septal defect

BACKGROUND

A gain-of-function mutation in germline ABL1 causes a syndrome including congenital heart defects. However, the molecular mechanisms of this syndrome remain unknown. In this study, we found a novel ABL1 mutation in a Japanese family with ventricular septal defect, finger contracture, skin abnormalities and failure to thrive, and the molecular mechanisms of these phenotypes were investigated.

METHODS AND RESULTS

Whole-exome sequencing on several family members revealed a novel mutation (c.1522A > C, p.I508L) in the tyrosine kinase domain of ABL1, and complete co-segregation with clinical presentations was confirmed in all members. Wild-type and mutant ABL1 were transfected into human embryonic kidney 293 cells for functional analysis. Western blotting confirmed that tyrosine phosphorylation in STAT5, a substrate of ABL1, was enhanced, and the novel mutation was proved to be a gain-of-function mutation. Since this novel mutation in ABL1 enhances tyrosine kinase activity, phosphorylated proteome analysis was used to elucidate the molecular pathology. The proteome analysis showed that phosphorylation in proteins such as UFD1, AXIN1, ATRX, which may be involved in the phenotypes, was enhanced in the mutant group.

CONCLUSIONS

The onset of congenital heart defects associated with this syndrome appears to involve a mechanism caused by UFD1 common to 22q.11.2 deletion syndrome. On the other hand, AXIN1 and ATRX may be important in elucidating the mechanisms of other phenotypes, such as finger contracture and failure to thrive. Verification of these hypotheses would lead to further understanding of the pathophysiology and the development of treatment methods.

Determination of chemical-disease risk values to prioritize connections between environmental factors, genetic variants, and human diseases

Traditional methods for chemical risk assessment are too time-consuming and resource-intensive to characterize either the diversity of chemicals to which humans are exposed or how that diversity may manifest in population susceptibility differences. The advent of novel toxicological data sources and their integration with bioinformatic databases affords opportunities for modern approaches that consider gene-environment (GxE) interactions in population risk assessment. Here, we present an approach that systematically links multiple data sources to relate chemical risk values to diseases and gene-disease variants. These data sources include high-throughput screening (HTS) results from Tox21/ToxCast, chemical-disease relationships from the Comparative Toxicogenomics Database (CTD), hazard data from resources like the Integrated Risk Information System, exposure data from the ExpoCast initiative, and gene-variant-disease information from the DisGeNET database. We use these integrated data to identify variants implicated in chemical-disease enrichments and develop a new value that estimates the risk of these associations toward differential population responses. Finally, we use this value to prioritize chemical-disease associations by exploring the genomic distribution of variants implicated in high-risk diseases. We offer this modular approach, termed DisQGOS (Disease Quotient Genetic Overview Score), for relating overall chemical-disease risk to potential for population variable responses, as a complement to methods aiming to modernize aspects of risk assessment.

SFRP1 is a possible candidate for epigenetic therapy in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) remains a lethal disease despite many proposed treatments. Recent studies have indicated that epigenetic therapy, which targets epigenetic effects, might be a new therapeutic methodology for NSCLC. However, it is not clear which objects (e.g., genes) this treatment specifically targets. Secreted frizzled-related proteins (SFRPs) are promising candidates for epigenetic therapy in many cancers, but there have been no reports of SFRPs targeted by epigenetic therapy for NSCLC.

Methods

This study performed a meta-analysis of reprogrammed NSCLC cell lines instead of the direct examination of epigenetic therapy treatment to identify epigenetic therapy targets. In addition, mRNA expression/promoter methylation profiles were processed by recently proposed principal component analysis based unsupervised feature extraction and categorical regression analysis based feature extraction.

Results

The Wnt/β-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target β-catenin, and thus might be targeted by epigenetic therapy in NSCLC cell lines. A histone deacetylase inhibitor might reactivate SFRP1 based upon the re-analysis of a public domain data set. Numerical computation validated the binding of SFRP1 to WNT1 to suppress Wnt signalling pathway activation in NSCLC.

Conclusions

The meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0196-3) contains supplementary material, which is available to authorized users.

A Comprehensive Evaluation of Disease Phenotype Networks for Gene Prioritization

Identification of disease-causing genes is a fundamental challenge for human health studies. The phenotypic similarity among diseases may reflect the interactions at the molecular level, and phenotype comparison can be used to predict disease candidate genes. Online Mendelian Inheritance in Man (OMIM) is a database of human genetic diseases and related genes that has become an authoritative source of disease phenotypes. However, disease phenotypes have been described by free text; thus, standardization of phenotypic descriptions is needed before diseases can be compared. Several disease phenotype networks have been established in OMIM using different standardization methods. Two of these networks are important for phenotypic similarity analysis: the first and most commonly used network (mimMiner) is standardized by medical subject heading, and the other network (resnikHPO) is the first to be standardized by human phenotype ontology. This paper comprehensively evaluates for the first time the accuracy of these two networks in gene prioritization based on protein–protein interactions using large-scale, leave-one-out cross-validation experiments. The results show that both networks can effectively prioritize disease-causing genes, and the approach that relates two diseases using a logistic function improves prioritization performance. Tanimoto, one of four methods for normalizing resnikHPO, generates a symmetric network and it performs similarly to mimMiner. Furthermore, an integration of these two networks outperforms either network alone in gene prioritization, indicating that these two disease networks are complementary.

Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population

Bovine leukosis virus is an oncogenic virus that infects B cells, causing bovine leukosis disease. This disease is known to have a negative impact on dairy cattle production and, because no treatment or vaccine is available, finding a possible genetic solution is important. Our objective was to perform a comprehensive genetic analysis of leukosis incidence in dairy cattle. Data on leukosis occurrence, pedigree and molecular information were combined into multitrait GBLUP models with milk yield (MY) and somatic cell score (SCS) to estimate genetic parameters and to perform whole-genome scans and pathway analysis. Leukosis data were available for 11 554 Holsteins daughters of 3002 sires from 112 herds in 16 US states. Genotypes from a 60K SNP panel were available for 961 of those bulls as well as for 2039 additional bulls. Heritability for leukosis incidence was estimated at about 8%, and the genetic correlations of leukosis disease incidence with MY and SCS were moderate at 0.18 and 0.20 respectively. The genome-wide scan indicated that leukosis is a complex trait, possibly modulated by many genes. The gene set analysis identified many functional terms that showed significant enrichment of genes associated with leukosis. Many of these terms, such as G-Protein Coupled Receptor Signaling Pathway, Regulation of Nucleotide Metabolic Process and different calcium-related processes, are known to be related to retrovirus infection. Overall, our findings contribute to a better understanding of the genetic architecture of this complex disease. The functional categories associated with leukosis may be useful in future studies on fine mapping of genes and development of dairy cattle breeding strategies.

An application of MeSH enrichment analysis in livestock

An integral part of functional genomics studies is to assess the enrichment of specific biological terms in lists of genes found to be playing an important role in biological phenomena. Contrasting the observed frequency of annotated terms with those of the background is at the core of overrepresentation analysis (ORA). Gene Ontology (GO) is a means to consistently classify and annotate gene products and has become a mainstay in ORA. Alternatively, Medical Subject Headings (MeSH) offers a comprehensive life science vocabulary including additional categories that are not covered by GO. Although MeSH is applied predominantly in human and model organism research, its full potential in livestock genetics is yet to be explored. In this study, MeSH ORA was evaluated to discern biological properties of identified genes and contrast them with the results obtained from GO enrichment analysis. Three published datasets were employed for this purpose, representing a gene expression study in dairy cattle, the use of SNPs for genome‐wide prediction in swine and the identification of genomic regions targeted by selection in horses. We found that several overrepresented MeSH annotations linked to these gene sets share similar concepts with those of GO terms. Moreover, MeSH yielded unique annotations, which are not directly provided by GO terms, suggesting that MeSH has the potential to refine and enrich the representation of biological knowledge. We demonstrated that MeSH can be regarded as another choice of annotation to draw biological inferences from genes identified via experimental analyses. When used in combination with GO terms, our results indicate that MeSH can enhance our functional interpretations for specific biological conditions or the genetic basis of complex traits in livestock species.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease

Background

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems.

Results

Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods.

Conclusions

Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0574-4) contains supplementary material, which is available to authorized users.

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis

Background

In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons.

Results

In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr.

Conclusions

Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0453-z) contains supplementary material, which is available to authorized users.

Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) remains lethal despite the development of numerous drug therapy technologies. About 85% to 90% of lung cancers are NSCLC and the 5-year survival rate is at best still below 50%. Thus, it is important to find drugable target genes for NSCLC to develop an effective therapy for NSCLC.

Results

Integrated analysis of publically available gene expression and promoter methylation patterns of two highly aggressive NSCLC cell lines generated by in vivo selection was performed. We selected eleven critical genes that may mediate metastasis using recently proposed principal component analysis based unsupervised feature extraction. The eleven selected genes were significantly related to cancer diagnosis. The tertiary protein structure of the selected genes was inferred by Full Automatic Modeling System, a profile-based protein structure inference software, to determine protein functions and to specify genes that could be potential drug targets.

Conclusions

We identified eleven potentially critical genes that may mediate NSCLC metastasis using bioinformatic analysis of publically available data sets. These genes are potential target genes for the therapy of NSCLC. Among the eleven genes, TINAGL1 and B3GALNT1 are possible candidates for drug compounds that inhibit their gene expression.

Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets

Background

Aberrant DNA methylation is often associated with cancers. Thus, screening genes with cancer-associated aberrant DNA methylation is a useful method to identify candidate cancer-causing genes. Aberrant DNA methylation is also genotype dependent. Thus, the selection of genes with genotype-specific aberrant DNA methylation in cancers is potentially important for tailor-made medicine. The selected genes are important candidate drug targets.

Results

The recently proposed principal component analysis based selection of genes with aberrant DNA methylation was applied to genotype and DNA methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism (SNP) arrays. SNPs that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. Thus, genes with genotype-specific DNA methylation patterns will be good therapeutic candidates. The tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, FAMS and Phyre2. Candidate drugs for three of these proteins, tyrosine kinase receptor (ALK), EGLN3 protein, and NUAK family SNF1-like kinase 1 (NUAK1), were identified by ChooseLD.

Conclusions

We detected genes with genotype-specific DNA methylation in squamous cell carcinoma that are candidate drug targets. Using in silico drug discovery, we successfully identified several candidate drugs for the ALK, EGLN3 and NUAK1 genes that displayed genotype-specific DNA methylation.

Experimental design-based functional mining and characterization of high-throughput sequencing data in the sequence read archive

High-throughput sequencing technology, also called next-generation sequencing (NGS), has the potential to revolutionize the whole process of genome sequencing, transcriptomics, and epigenetics. Sequencing data is captured in a public primary data archive, the Sequence Read Archive (SRA). As of January 2013, data from more than 14,000 projects have been submitted to SRA, which is double that of the previous year. Researchers can download raw sequence data from SRA website to perform further analyses and to compare with their own data. However, it is extremely difficult to search entries and download raw sequences of interests with SRA because the data structure is complicated, and experimental conditions along with raw sequences are partly described in natural language. Additionally, some sequences are of inconsistent quality because anyone can submit sequencing data to SRA with no quality check. Therefore, as a criterion of data quality, we focused on SRA entries that were cited in journal articles. We extracted SRA IDs and PubMed IDs (PMIDs) from SRA and full-text versions of journal articles and retrieved 2748 SRA ID-PMID pairs. We constructed a publication list referring to SRA entries. Since, one of the main themes of -omics analyses is clarification of disease mechanisms, we also characterized SRA entries by disease keywords, according to the Medical Subject Headings (MeSH) extracted from articles assigned to each SRA entry. We obtained 989 SRA ID-MeSH disease term pairs, and constructed a disease list referring to SRA data. We previously developed feature profiles of diseases in a system called “Gendoo”. We generated hyperlinks between diseases extracted from SRA and the feature profiles of it. The developed project, publication and disease lists resulting from this study are available at our web service, called “DBCLS SRA” (http://sra.dbcls.jp/). This service will improve accessibility to high-quality data from SRA.

Predicting whole genome protein interaction networks from primary sequence data in model and non-model organisms using ENTS

Background

The large-scale identification of physical protein-protein interactions (PPIs) is an important step toward understanding how biological networks evolve and generate emergent phenotypes. However, experimental identification of PPIs is a laborious and error-prone process, and current methods of PPI prediction tend to be highly conservative or require large amounts of functional data that may not be available for newly-sequenced organisms.

Results

In this study we demonstrate a random-forest based technique, ENTS, for the computational prediction of protein-protein interactions based only on primary sequence data. Our approach is able to efficiently predict interactions on a whole-genome scale for any eukaryotic organism, using pairwise combinations of conserved domains and predicted subcellular localization of proteins as input features. We present the first predicted interactome for the forest tree Populus trichocarpa in addition to the predicted interactomes for Saccharomyces cerevisiae, Homo sapiens, Mus musculus, and Arabidopsis thaliana. Comparing our approach to other PPI predictors, we find that ENTS performs comparably to or better than a number of existing approaches, including several that utilize a variety of functional information for their predictions. We also find that the predicted interactions are biologically meaningful, as indicated by similarity in functional annotations and enrichment of co-expressed genes in public microarray datasets. Furthermore, we demonstrate some of the biological insights that can be gained from these predicted interaction networks. We show that the predicted interactions yield informative groupings of P. trichocarpa metabolic pathways, literature-supported associations among human disease states, and theory-supported insight into the evolutionary dynamics of duplicated genes in paleopolyploid plants.

Conclusion

We conclude that the ENTS classifier will be a valuable tool for the de novo annotation of genome sequences, providing initial clues about regulatory and metabolic network topology, and revealing relationships that are not immediately obvious from traditional homology-based annotations.

'Omics' approaches to understanding interstitial cystitis/painful bladder syndrome/bladder pain syndrome

Recent efforts in the generation of large genomics, transcriptomics, proteomics, metabolomics and other types of 'omics' data sets have provided an unprecedentedly detailed view of certain diseases, however to date most of this literature has been focused on malignancy and other lethal pathological conditions. Very little intensive work on global profiles has been performed to understand the molecular mechanism of interstitial cystitis/painful bladder syndrome/bladder pain syndrome (IC/PBS/BPS), a chronic lower urinary tract disorder characterized by pelvic pain, urinary urgency and frequency, which can lead to long lasting adverse effects on quality of life. A lack of understanding of molecular mechanism has been a challenge and dilemma for diagnosis and treatment, and has also led to a delay in basic and translational research focused on biomarker and drug discovery, clinical therapy, and preventive strategies against IC/PBS/BPS. This review describes the current state of 'omics' studies and available data sets relevant to IC/PBS/BPS, and presents opportunities for new research directed at understanding the pathogenesis of this complex condition.

A systems approach to rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily attacks synovial joints. Despite the advances in diagnosis and treatment of RA, novel molecular targets are still needed to improve the accuracy of diagnosis and the therapeutic outcomes. Here, we present a systems approach that can effectively 1) identify core RA-associated genes (RAGs), 2) reconstruct RA-perturbed networks, and 3) select potential targets for diagnosis and treatments of RA. By integrating multiple gene expression datasets previously reported, we first identified 983 core RAGs that show RA dominant differential expression, compared to osteoarthritis (OA), in the multiple datasets. Using the core RAGs, we then reconstructed RA-perturbed networks that delineate key RA associated cellular processes and transcriptional regulation. The networks revealed that synovial fibroblasts play major roles in defining RA-perturbed processes, anti-TNF-α therapy restored many RA-perturbed processes, and 19 transcription factors (TFs) have major contribution to deregulation of the core RAGs in the RA-perturbed networks. Finally, we selected a list of potential molecular targets that can act as metrics or modulators of the RA-perturbed networks. Therefore, these network models identify a panel of potential targets that will serve as an important resource for the discovery of therapeutic targets and diagnostic markers, as well as providing novel insights into RA pathogenesis.

Evaluation of the testicular toxicity of prenatal exposure to bisphenol A based on microarray analysis combined with MeSH annotation

Bisphenol A (BPA) is known to be an endocrine disruptor that affects the development of reproductive system. The aim of the present study was to investigate a group of testicular genes dysregulated by prenatal exposure to BPA. Pregnant ICR mice were treated with BPA by subcutaneous administration on days 7 and 14 of pregnancy. Tissue and blood samples were collected from 6-week-old male offspring. Testes were subjected to gene expression analysis using a testis-specific microarray (Testis2), consisting of 2,482 mouse cDNA clones annotated with Medical Subject Headings (MeSH) terms indicative of testicular components and functions. To interpret the microarray data, we used the MeSH terms significantly associated with the altered genes. As a result, MeSH terms related to androgens and Sertoli cells were extracted in BPA-treated groups. Among the genes related to Sertoli cells, downregulation of Msi1h, Ncoa1, Nid1, Hspb2, and Gata6 were detected in the testis of mice treated with BPA (twice administered 50 mg/kg). The MeSH terms associated with this group of genes may provide useful means to interpret the testicular toxicity of BPA. This article concludes that prenatal BPA exposure downregulates expression of genes associated with Sertoli cell function and affects the reproductive function of male offspring. Additionally, a method using MeSH to extract a group of genes was useful for predicting the testicular and reproductive toxicity of prenatal BPA exposure.

Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles

BACKGROUND

MEDLINE(®)/PubMed(®) currently indexes over 18 million biomedical articles, providing unprecedented opportunities and challenges for text analysis. Using Medical Subject Heading Over-representation Profiles (MeSHOPs), an entity of interest can be robustly summarized, quantitatively identifying associated biomedical terms and predicting novel indirect associations.

METHODS

A procedure is introduced for quantitative comparison of MeSHOPs derived from a group of MEDLINE(®) articles for a biomedical topic (for example, articles for a specific gene or disease). Similarity scores are computed to compare MeSHOPs of genes and diseases.

RESULTS

Similarity scores successfully infer novel associations between diseases and genes. The number of papers addressing a gene or disease has a strong influence on predicted associations, revealing an important bias for gene-disease relationship prediction. Predictions derived from comparisons of MeSHOPs achieves a mean 8% AUC improvement in the identification of gene-disease relationships compared to gene-independent baseline properties.

CONCLUSIONS

MeSHOP comparisons are demonstrated to provide predictive capacity for novel relationships between genes and human diseases. We demonstrate the impact of literature bias on the performance of gene-disease prediction methods. MeSHOPs provide a rich source of annotation to facilitate relationship discovery in biomedical informatics.

Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs)

BACKGROUND

MEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships.

RESULTS

We introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms.

CONCLUSIONS

MeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.

Tutorial videos of bioinformatics resources: online distribution trial in Japan named TogoTV

In recent years, biological web resources such as databases and tools have become more complex because of the enormous amounts of data generated in the field of life sciences. Traditional methods of distributing tutorials include publishing textbooks and posting web documents, but these static contents cannot adequately describe recent dynamic web services. Due to improvements in computer technology, it is now possible to create dynamic content such as video with minimal effort and low cost on most modern computers. The ease of creating and distributing video tutorials instead of static content improves accessibility for researchers, annotators and curators. This article focuses on online video repositories for educational and tutorial videos provided by resource developers and users. It also describes a project in Japan named TogoTV (http://togotv.dbcls.jp/en/) and discusses the production and distribution of high-quality tutorial videos, which would be useful to viewer, with examples. This article intends to stimulate and encourage researchers who develop and use databases and tools to distribute how-to videos as a tool to enhance product usability.

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

Use of Medical Subject Headings (MeSH) in Portuguese for categorizing web-based healthcare content

INTRODUCTION

Internet users are increasingly using the worldwide web to search for information relating to their health. This situation makes it necessary to create specialized tools capable of supporting users in their searches.

OBJECTIVE

To apply and compare strategies that were developed to investigate the use of the Portuguese version of Medical Subject Headings (MeSH) for constructing an automated classifier for Brazilian Portuguese-language web-based content within or outside of the field of healthcare, focusing on the lay public.

METHODS

3658 Brazilian web pages were used to train the classifier and 606 Brazilian web pages were used to validate it. The strategies proposed were constructed using content-based vector methods for text classification, such that Naive Bayes was used for the task of classifying vector patterns with characteristics obtained through the proposed strategies.

RESULTS

A strategy named InDeCS was developed specifically to adapt MeSH for the problem that was put forward. This approach achieved better accuracy for this pattern classification task (0.94 sensitivity, specificity and area under the ROC curve).

CONCLUSIONS

Because of the significant results achieved by InDeCS, this tool has been successfully applied to the Brazilian healthcare search portal known as Busca Saúde. Furthermore, it could be shown that MeSH presents important results when used for the task of classifying web-based content focusing on the lay public. It was also possible to show from this study that MeSH was able to map out mutable non-deterministic characteristics of the web.